Session 2: Homework 1

1 Where Do People Drink The Most Beer, Wine And Spirits?

Back in 2014, fivethiryeight.com published an article on alchohol consumption in different countries. The data drinks is available as part of the fivethirtyeight package. Make sure you have installed the fivethirtyeight package before proceeding.

library(fivethirtyeight)
data(drinks)

# or download directly
# alcohol_direct <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/alcohol-consumption/drinks.csv")

What are the variable types? Any missing values we should worry about?

No missing values.

Column name	country	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol
Variable type	character	integer	integer	integer	double

glimpse(drinks)

## Rows: 193
## Columns: 5
## $ country                      <chr> "Afghanistan", "Albania", "Algeria", "And…
## $ beer_servings                <int> 0, 89, 25, 245, 217, 102, 193, 21, 261, 2…
## $ spirit_servings              <int> 0, 132, 0, 138, 57, 128, 25, 179, 72, 75,…
## $ wine_servings                <int> 0, 54, 14, 312, 45, 45, 221, 11, 212, 191…
## $ total_litres_of_pure_alcohol <dbl> 0.0, 4.9, 0.7, 12.4, 5.9, 4.9, 8.3, 3.8, …

skim(drinks)

Data summary

Name	drinks
Number of rows	193
Number of columns	5
_______________________
Column type frequency:
character	1
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
country	0	1	3	28	0	193	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
beer_servings	0	1	106.16	101.14	0	20.0	76.0	188.0	376.0	▇▃▂▂▁
spirit_servings	0	1	80.99	88.28	0	4.0	56.0	128.0	438.0	▇▃▂▁▁
wine_servings	0	1	49.45	79.70	0	1.0	8.0	59.0	370.0	▇▁▁▁▁
total_litres_of_pure_alcohol	0	1	4.72	3.77	0	1.3	4.2	7.2	14.4	▇▃▅▃▁

Make a plot that shows the top 25 beer consuming countries

drinks %>% 
  slice_max ( order_by = beer_servings, n=25 ) %>% # taking top 25 countries by servings
  ggplot(aes(x = beer_servings, y = fct_reorder(country, beer_servings))) +
  geom_col(fill="orange") +
  labs(
    title = "Top 25 Beer Serving Countries in 2010",
    subtitle = "Standard Servings Per Person",
    x = "Beer Servings (in cans)",
    y = "Country"
  )

Make a plot that shows the top 25 wine consuming countries

drinks %>% 
  slice_max ( order_by = wine_servings, n=25 ) %>% # taking top 25 countries by servings
  ggplot(aes(x = wine_servings, y = fct_reorder(country, wine_servings))) +
  geom_col(fill="dark red") +
  labs(
    title = "Top 25 Wine Serving Countries in 2010",
    subtitle = "Standard Servings Per Person",
    x = "Wine Servings (in glasses)",
    y = "Country"
  )

Finally, make a plot that shows the top 25 spirit consuming countries

drinks %>% 
  slice_max ( order_by = spirit_servings, n=25 ) %>% # taking top 25 countries by servings
  ggplot(aes(x = spirit_servings, y = fct_reorder(country, spirit_servings))) +
  geom_col(fill="grey") +
  labs(
    title = "Top 25 Spirit Serving Countries in 2010",
    subtitle = "Servings (in shots) Per Person",
    x = "Spirit Servings",
    y = "Country"
  )

What can you infer from these plots? Don’t just explain what’s in the graph, but speculate or tell a short story (1-2 paragraphs max).

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

1. European countries are high consumers of wine.

2. Beer is more evenly distributed around the world in the top 25 countries, as compared to wine and spirit.

3. European countries are higher ranked for overall consumption of beer, wine and spirit.

2 Analysis of movies- IMDB dataset

We will look at a subset sample of movies, taken from the Kaggle IMDB 5000 movie dataset

Besides the obvious variables of title, genre, director, year, and duration, the rest of the variables are as follows:

• gross : The gross earnings in the US box office, not adjusted for inflation
• budget: The movie’s budget
• cast_facebook_likes: the number of facebook likes cast members received
• votes: the number of people who voted for (or rated) the movie in IMDB
• reviews: the number of reviews for that movie
• rating: IMDB average rating

2.1 Use your data import, inspection, and cleaning skills to answer the following:

• Are there any missing values (NAs)? Are all entries distinct or are there duplicate entries?

movies <- read_csv(here::here("data", "movies.csv"))
glimpse(movies)

## Rows: 2,961
## Columns: 11
## $ title               <chr> "Avatar", "Titanic", "Jurassic World", "The Avenge…
## $ genre               <chr> "Action", "Drama", "Action", "Action", "Action", "…
## $ director            <chr> "James Cameron", "James Cameron", "Colin Trevorrow…
## $ year                <dbl> 2009, 1997, 2015, 2012, 2008, 1999, 1977, 2015, 20…
## $ duration            <dbl> 178, 194, 124, 173, 152, 136, 125, 141, 164, 93, 1…
## $ gross               <dbl> 7.61e+08, 6.59e+08, 6.52e+08, 6.23e+08, 5.33e+08, …
## $ budget              <dbl> 2.37e+08, 2.00e+08, 1.50e+08, 2.20e+08, 1.85e+08, …
## $ cast_facebook_likes <dbl> 4834, 45223, 8458, 87697, 57802, 37723, 13485, 920…
## $ votes               <dbl> 886204, 793059, 418214, 995415, 1676169, 534658, 9…
## $ reviews             <dbl> 3777, 2843, 1934, 2425, 5312, 3917, 1752, 1752, 35…
## $ rating              <dbl> 7.9, 7.7, 7.0, 8.1, 9.0, 6.5, 8.7, 7.5, 8.5, 7.2, …

# no missing values. There are Duplicates (2907 distinct titles in 2961 rows). 
skim(movies)

Data summary

Name	movies
Number of rows	2961
Number of columns	11
_______________________
Column type frequency:
character	3
numeric	8
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
title	0	1	1	83	0	2907	0
genre	0	1	5	11	0	17	0
director	0	1	3	32	0	1366	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2.00e+03	9.95e+00	1920.0	2.00e+03	2.00e+03	2.01e+03	2.02e+03	▁▁▁▂▇
duration	0	1	1.10e+02	2.22e+01	37.0	9.50e+01	1.06e+02	1.19e+02	3.30e+02	▃▇▁▁▁
gross	0	1	5.81e+07	7.25e+07	703.0	1.23e+07	3.47e+07	7.56e+07	7.61e+08	▇▁▁▁▁
budget	0	1	4.06e+07	4.37e+07	218.0	1.10e+07	2.60e+07	5.50e+07	3.00e+08	▇▂▁▁▁
cast_facebook_likes	0	1	1.24e+04	2.05e+04	0.0	2.24e+03	4.60e+03	1.69e+04	6.57e+05	▇▁▁▁▁
votes	0	1	1.09e+05	1.58e+05	5.0	1.99e+04	5.57e+04	1.33e+05	1.69e+06	▇▁▁▁▁
reviews	0	1	5.03e+02	4.94e+02	2.0	1.99e+02	3.64e+02	6.31e+02	5.31e+03	▇▁▁▁▁
rating	0	1	6.39e+00	1.05e+00	1.6	5.80e+00	6.50e+00	7.10e+00	9.30e+00	▁▁▆▇▁

# show the duplicate movies
movies %>% count(title, sort=T)

## # A tibble: 2,907 × 2
##    title                           n
##    <chr>                       <int>
##  1 Home                            3
##  2 A Nightmare on Elm Street       2
##  3 Across the Universe             2
##  4 Alice in Wonderland             2
##  5 Aloha                           2
##  6 Around the World in 80 Days     2
##  7 Brothers                        2
##  8 Carrie                          2
##  9 Chasing Liberty                 2
## 10 Cinderella                      2
## # … with 2,897 more rows

# to see what happens with the duplicates
movies %>% filter(title=="Home")

## # A tibble: 3 × 11
##   title genre     director   year duration   gross budget cast_facebook_l… votes
##   <chr> <chr>     <chr>     <dbl>    <dbl>   <dbl>  <dbl>            <dbl> <dbl>
## 1 Home  Adventure Tim John…  2015       94  1.77e8 1.35e8            17883 70121
## 2 Home  Adventure Tim John…  2015       94  1.77e8 1.35e8            17883 70133
## 3 Home  Adventure Tim John…  2015       94  1.77e8 1.35e8            17883 70136
## # … with 2 more variables: reviews <dbl>, rating <dbl>

# `distinct` function can only keep the first entry but not latest
# movies <- distinct(movies, title, .keep_all=T)
length(unique(movies$title))

## [1] 2907

movies <- movies %>% 
  group_by(title) %>% 
  filter(votes == max(votes)) %>%
  ungroup()

# there are still duplicates
movies %>% count(title, sort=T)

## # A tibble: 2,907 × 2
##    title                          n
##    <chr>                      <int>
##  1 Chasing Liberty                2
##  2 10 Cloverfield Lane            1
##  3 10 Days in a Madhouse          1
##  4 10 Things I Hate About You     1
##  5 102 Dalmatians                 1
##  6 10th & Wolf                    1
##  7 12 Rounds                      1
##  8 12 Years a Slave               1
##  9 127 Hours                      1
## 10 13 Going on 30                 1
## # … with 2,897 more rows

# to see what happens with the duplicates
movies %>% filter(title=="Chasing Liberty")

## # A tibble: 2 × 11
##   title    genre  director   year duration   gross budget cast_facebook_l… votes
##   <chr>    <chr>  <chr>     <dbl>    <dbl>   <dbl>  <dbl>            <dbl> <dbl>
## 1 Chasing… Comedy Andy Cad…  2004      101  1.22e7  2.3e7              842 30092
## 2 Chasing… Comedy Andy Cad…  2004      101  1.22e7  2.3e7              829 30092
## # … with 2 more variables: reviews <dbl>, rating <dbl>

# do the filter only for the entries of Chasing Liberty 
movies <- movies %>%
  group_by(title) %>% 
  filter(cast_facebook_likes==max(cast_facebook_likes)) %>%
  ungroup()

skim(movies)

Data summary

Name	movies
Number of rows	2907
Number of columns	11
_______________________
Column type frequency:
character	3
numeric	8
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
title	0	1	1	83	0	2907	0
genre	0	1	5	11	0	17	0
director	0	1	3	32	0	1366	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2.00e+03	9.92e+00	1920.0	2.00e+03	2.00e+03	2.01e+03	2.02e+03	▁▁▁▂▇
duration	0	1	1.10e+02	2.23e+01	37.0	9.50e+01	1.05e+02	1.19e+02	3.30e+02	▃▇▁▁▁
gross	0	1	5.76e+07	7.23e+07	703.0	1.20e+07	3.45e+07	7.51e+07	7.61e+08	▇▁▁▁▁
budget	0	1	4.02e+07	4.32e+07	218.0	1.10e+07	2.50e+07	5.50e+07	3.00e+08	▇▂▁▁▁
cast_facebook_likes	0	1	1.23e+04	2.05e+04	0.0	2.22e+03	4.54e+03	1.68e+04	6.57e+05	▇▁▁▁▁
votes	0	1	1.09e+05	1.59e+05	5.0	1.95e+04	5.47e+04	1.32e+05	1.69e+06	▇▁▁▁▁
reviews	0	1	4.98e+02	4.93e+02	2.0	1.97e+02	3.58e+02	6.24e+02	5.31e+03	▇▁▁▁▁
rating	0	1	6.39e+00	1.06e+00	1.6	5.80e+00	6.50e+00	7.10e+00	9.30e+00	▁▁▆▇▁

1. There are no missing values in the dataset.
2. The movies with duplicated entries and how we cleaned them are shown as above.

• Produce a table with the count of movies by genre, ranked in descending order

movies %>% count(genre, sort = TRUE)

## # A tibble: 17 × 2
##    genre           n
##    <chr>       <int>
##  1 Comedy        844
##  2 Action        719
##  3 Drama         484
##  4 Adventure     281
##  5 Crime         198
##  6 Biography     135
##  7 Horror        128
##  8 Animation      35
##  9 Fantasy        26
## 10 Documentary    25
## 11 Mystery        15
## 12 Sci-Fi          7
## 13 Family          3
## 14 Musical         2
## 15 Romance         2
## 16 Western         2
## 17 Thriller        1

• Produce a table with the average gross earning and budget (gross and budget) by genre. Calculate a variable return_on_budget which shows how many $ did a movie make at the box office for each $ of its budget. Ranked genres by this return_on_budget in descending order

movies %>% 
  mutate(movies_return = gross/budget ) %>%
  group_by(genre) %>%
  summarise(avg_gross = mean(gross),
            avg_budget = mean(budget),
            genre_return_on_budget = sum(gross)/sum(budget),
            movie_mean_return_on_budget = mean(movies_return)) %>%
  arrange(-movie_mean_return_on_budget)

## # A tibble: 17 × 5
##    genre        avg_gross avg_budget genre_return_on_budget movie_mean_return_o…
##    <chr>            <dbl>      <dbl>                  <dbl>                <dbl>
##  1 Horror       37782310.  13804379.                2.74                86.1    
##  2 Biography    45201805.  28543696.                1.58                22.3    
##  3 Musical      92084000    3189500                28.9                 18.8    
##  4 Family      149160478.  14833333.               10.1                 14.1    
##  5 Documentary  17353973.   5887852.                2.95                 8.70   
##  6 Western      20821884    3465000                 6.01                 7.06   
##  7 Fantasy      41902674.  18484615.                2.27                 6.10   
##  8 Animation    98433792.  61701429.                1.60                 5.01   
##  9 Comedy       42487808.  24458506.                1.74                 3.70   
## 10 Romance      31264848.  25107500                 1.25                 3.17   
## 11 Drama        36754959.  25832605.                1.42                 2.98   
## 12 Mystery      69117136.  41500000                 1.67                 2.90   
## 13 Adventure    94350236.  64692313.                1.46                 2.44   
## 14 Crime        37601525.  26527405.                1.42                 2.19   
## 15 Action       86270343.  70774558.                1.22                 1.93   
## 16 Sci-Fi       29788371.  27607143.                1.08                 1.58   
## 17 Thriller         2468     300000                 0.00823              0.00823

• Produce a table that shows the top 15 directors who have created the highest gross revenue in the box office. Don’t just show the total gross amount, but also the mean, median, and standard deviation per director.

movies %>%
  group_by(director) %>%
  summarise(total_gross = sum(gross),
            mean_gross = mean(gross),
            median_gross = median(gross),
            standard_dev_gross = sd(gross)) %>%
  slice_max ( order_by = total_gross, n = 15)

## # A tibble: 15 × 5
##    director          total_gross mean_gross median_gross standard_dev_gross
##    <chr>                   <dbl>      <dbl>        <dbl>              <dbl>
##  1 Steven Spielberg   4014061704 174524422.   164435221          101421051.
##  2 Michael Bay        2195443511 182953626.   168468240.         125789167.
##  3 James Cameron      1909725910 318287652.   175562880.         309171337.
##  4 Christopher Nolan  1813227576 226653447    196667606.         187224133.
##  5 George Lucas       1741418480 348283696    380262555          146193880.
##  6 Robert Zemeckis    1619309108 124562239.   100853835           91300279.
##  7 Tim Burton         1557078534 111219895.    69791834           99304293.
##  8 Sam Raimi          1443167519 180395940.   138480208          174705230.
##  9 Clint Eastwood     1378321100  72543216.    46700000           75487408.
## 10 Francis Lawrence   1358501971 271700394.   281666058          135437020.
## 11 Ron Howard         1335988092 111332341    101587923           81933761.
## 12 Gore Verbinski     1329600995 189942999.   123207194          154473822.
## 13 Andrew Adamson     1137446920 284361730    279680930.         120895765.
## 14 Shawn Levy         1129750988 102704635.    85463309           65484773.
## 15 Ridley Scott       1128857598  80632686.    47775715           68812285.

• Finally, ratings. Produce a table that describes how ratings are distributed by genre. We don’t want just the mean, but also, min, max, median, SD and some kind of a histogram or density graph that visually shows how ratings are distributed.

movies_rating <- movies %>%
  group_by(genre) %>%
  summarise(mean_rating = mean(rating),
            min_rating = min(rating),
             max_rating = max(rating),
             sd_rating = sd(rating)) 
movies_rating

## # A tibble: 17 × 5
##    genre       mean_rating min_rating max_rating sd_rating
##    <chr>             <dbl>      <dbl>      <dbl>     <dbl>
##  1 Action             6.23        2.1        9       1.04 
##  2 Adventure          6.51        2.3        8.6     1.11 
##  3 Animation          6.65        4.5        8       0.968
##  4 Biography          7.11        4.5        8.9     0.760
##  5 Comedy             6.11        1.9        8.8     1.02 
##  6 Crime              6.92        4.8        9.3     0.853
##  7 Documentary        6.66        1.6        8.5     1.77 
##  8 Drama              6.74        2.1        8.8     0.915
##  9 Family             6.5         5.7        7.9     1.22 
## 10 Fantasy            6.08        4.3        7.9     0.953
## 11 Horror             5.79        3.6        8.5     0.987
## 12 Musical            6.75        6.3        7.2     0.636
## 13 Mystery            6.84        4.6        8.5     0.910
## 14 Romance            6.65        6.2        7.1     0.636
## 15 Sci-Fi             6.66        5          8.2     1.09 
## 16 Thriller           4.8         4.8        4.8    NA    
## 17 Western            5.7         4.1        7.3     2.26

movies %>%
  ggplot(mapping = aes(x = rating)) + 
  geom_histogram(bins=30) +
  facet_wrap(~genre)+
  labs(title = "Distribution of ratings in each genre",
       x = "Rating (1-10)",
       y = "Num of movies")+
  NULL

2.2 Use ggplot to answer the following

• Examine the relationship between gross and cast_facebook_likes. Produce a scatterplot and write one sentence discussing whether the number of facebook likes that the cast has received is likely to be a good predictor of how much money a movie will make at the box office. What variable are you going to map to the Y- and X- axes?

ggplot(movies, aes(x = cast_facebook_likes, y = gross)) +
  geom_point() +
  geom_smooth(method = "lm")+
  NULL

1. Facebook likes do not seem like a good indicator of the gross as there is no direct correlation as seen from the scatter plot.
2. We mapped gross to Y axes and number of facebook likes to X, because the gross is the final outcome of a movie, aka dependent variable.

• Examine the relationship between gross and budget. Produce a scatterplot and write one sentence discussing whether budget is likely to be a good predictor of how much money a movie will make at the box office.

ggplot(movies, aes(x = budget , y = gross)) +
  geom_point() +
  geom_smooth(method = "lm")

• The budget and gross do seem correlated. The higher the budget, it is more likely that the gross may be higher.

• Examine the relationship between gross and rating. Produce a scatterplot, faceted by genre and discuss whether IMDB ratings are likely to be a good predictor of how much money a movie will make at the box office. Is there anything strange in this dataset?

ggplot(movies, aes(x = rating , y = gross)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~genre) +
  labs(title = "Gross vs Rating of Movies For Each Genre ",
       x = "Rating",
       y = "Gross") +
  NULL

We can see that:

• The higher the rating the more will be the gross for the most genres of movies.

• For movies of some genres like ‘Documentary’, ‘Mystery’, ‘Horror’ and ‘Sci-Fi’, the gross has a very less change with respect to rating. Documentaries certainly have a different business model.

• Negative correlation even appears.

3 Returns of financial stocks

You may find useful the material on finance data sources.

We will use the tidyquant package to download historical data of stock prices, calculate returns, and examine the distribution of returns.

We must first identify which stocks we want to download data for, and for this we must know their ticker symbol; Apple is known as AAPL, Microsoft as MSFT, McDonald’s as MCD, etc. The file nyse.csv contains 508 stocks listed on the NYSE, their ticker symbol, name, the IPO (Initial Public Offering) year, and the sector and industry the company is in.

nyse <- read_csv(here::here("data","nyse.csv"))

glimpse(nyse)

## Rows: 508
## Columns: 6
## $ symbol        <chr> "MMM", "ABB", "ABT", "ABBV", "ACN", "AAP", "AFL", "A", "…
## $ name          <chr> "3M Company", "ABB Ltd", "Abbott Laboratories", "AbbVie …
## $ ipo_year      <chr> "n/a", "n/a", "n/a", "2012", "2001", "n/a", "n/a", "1999…
## $ sector        <chr> "Health Care", "Consumer Durables", "Health Care", "Heal…
## $ industry      <chr> "Medical/Dental Instruments", "Electrical Products", "Ma…
## $ summary_quote <chr> "https://www.nasdaq.com/symbol/mmm", "https://www.nasdaq…

nyse$sector

##   [1] "Health Care"           "Consumer Durables"     "Health Care"          
##   [4] "Health Care"           "Miscellaneous"         "Consumer Services"    
##   [7] "Finance"               "Capital Goods"         "Basic Industries"     
##  [10] "Basic Industries"      "Health Care"           "Consumer Services"    
##  [13] "Miscellaneous"         "Finance"               "Health Care"          
##  [16] "Finance"               "Finance"               "Consumer Services"    
##  [19] "Consumer Non-Durables" "Consumer Non-Durables" "Consumer Durables"    
##  [22] "Public Utilities"      "Public Utilities"      "Public Utilities"     
##  [25] "Public Utilities"      "Finance"               "Finance"              
##  [28] "Consumer Services"     "Public Utilities"      "Finance"              
##  [31] "Health Care"           "Capital Goods"         "Consumer Durables"    
##  [34] "Consumer Non-Durables" "Consumer Services"     "Health Care"          
##  [37] "Finance"               "Finance"               "Capital Goods"        
##  [40] "Consumer Services"     "Basic Industries"      "Consumer Non-Durables"
##  [43] "Capital Goods"         "Technology"            "Finance"              
##  [46] "Health Care"           "Public Utilities"      "Public Utilities"     
##  [49] "Technology"            "Consumer Services"     "Consumer Services"    
##  [52] "Public Utilities"      "Finance"               "Energy"               
##  [55] "Consumer Durables"     "Finance"               "Finance"              
##  [58] "Finance"               "Finance"               "Finance"              
##  [61] "Finance"               "Finance"               "Finance"              
##  [64] "Finance"               "Finance"               "Finance"              
##  [67] "Finance"               "Finance"               "Basic Industries"     
##  [70] "Health Care"           "Finance"               "Public Utilities"     
##  [73] "Health Care"           "Consumer Services"     "Basic Industries"     
##  [76] "Energy"                "Capital Goods"         "Finance"              
##  [79] "Capital Goods"         "Consumer Services"     "Consumer Services"    
##  [82] "Health Care"           "Energy"                "Health Care"          
##  [85] "Consumer Non-Durables" "Miscellaneous"         "Consumer Services"    
##  [88] "Consumer Services"     "Finance"               "Public Utilities"     
##  [91] "Consumer Services"     "Energy"                "Consumer Services"    
##  [94] "Consumer Non-Durables" "Finance"               "Transportation"       
##  [97] "Energy"                "Transportation"        "Miscellaneous"        
## [100] "Finance"               "Health Care"           "Consumer Durables"    
## [103] "Consumer Services"     "Consumer Services"     "Consumer Durables"    
## [106] "Capital Goods"         "Finance"               "Consumer Services"    
## [109] "Basic Industries"      "Energy"                "Health Care"          
## [112] "Public Utilities"      "Public Utilities"      "Public Utilities"     
## [115] "Basic Industries"      "Consumer Services"     "Energy"               
## [118] "Consumer Services"     "Finance"               "Public Utilities"     
## [121] "Energy"                "Public Utilities"      "Public Utilities"     
## [124] "Consumer Services"     "Finance"               "Public Utilities"     
## [127] "Basic Industries"      "Health Care"           "Finance"              
## [130] "Finance"               "Consumer Durables"     "Public Utilities"     
## [133] "Finance"               "Capital Goods"         "Consumer Non-Durables"
## [136] "Consumer Non-Durables" "Consumer Non-Durables" "Consumer Non-Durables"
## [139] "Consumer Non-Durables" "Energy"                "Energy"               
## [142] "Public Utilities"      "Consumer Non-Durables" "Energy"               
## [145] "Basic Industries"      "Consumer Non-Durables" "Finance"              
## [148] "Finance"               "Capital Goods"         "Consumer Services"    
## [151] "Energy"                "Health Care"           "Capital Goods"        
## [154] "Capital Goods"         "Consumer Services"     "Capital Goods"        
## [157] "Technology"            "Transportation"        "Finance"              
## [160] "Consumer Non-Durables" "Consumer Services"     "Finance"              
## [163] "Consumer Services"     "Public Utilities"      "Technology"           
## [166] "Basic Industries"      "Public Utilities"      "Public Utilities"     
## [169] "Consumer Services"     "Basic Industries"      "Technology"           
## [172] "Basic Industries"      "Energy"                "Public Utilities"     
## [175] "Health Care"           "Health Care"           "Health Care"          
## [178] "Energy"                "Energy"                "Public Utilities"     
## [181] "Public Utilities"      "Energy"                "Public Utilities"     
## [184] "Public Utilities"      "Energy"                "Technology"           
## [187] "Finance"               "Energy"                "Consumer Services"    
## [190] "Consumer Services"     "Consumer Services"     "Consumer Non-Durables"
## [193] "Finance"               "Public Utilities"      "Public Utilities"     
## [196] "Public Utilities"      "Consumer Services"     "Energy"               
## [199] "Technology"            "Miscellaneous"         "Consumer Services"    
## [202] "Transportation"        "Capital Goods"         "Capital Goods"        
## [205] "Finance"               "Miscellaneous"         "Public Utilities"     
## [208] "Miscellaneous"         "Basic Industries"      "Consumer Non-Durables"
## [211] "Capital Goods"         "Public Utilities"      "Capital Goods"        
## [214] "Basic Industries"      "Finance"               "Basic Industries"     
## [217] "Health Care"           "Consumer Services"     "Capital Goods"        
## [220] "Energy"                "Consumer Non-Durables" "Capital Goods"        
## [223] "Capital Goods"         "Health Care"           "Miscellaneous"        
## [226] "Finance"               "Technology"            "Finance"              
## [229] "Energy"                "Finance"               "Health Care"          
## [232] "Consumer Services"     "Finance"               "Capital Goods"        
## [235] "Consumer Non-Durables" "Energy"                "Technology"           
## [238] "Consumer Services"     "Consumer Services"     "Capital Goods"        
## [241] "Capital Goods"         "Consumer Non-Durables" "Consumer Services"    
## [244] "Technology"            "Finance"               "Health Care"          
## [247] "Finance"               "Capital Goods"         "Technology"           
## [250] "Technology"            "Technology"            "Finance"              
## [253] "Capital Goods"         "Finance"               "Consumer Services"    
## [256] "Technology"            "Basic Industries"      "Basic Industries"     
## [259] "Finance"               "Health Care"           "Finance"              
## [262] "Finance"               "Consumer Non-Durables" "Basic Industries"     
## [265] "Health Care"           "Consumer Services"     "Transportation"       
## [268] "Finance"               "Consumer Non-Durables" "Consumer Non-Durables"
## [271] "Finance"               "Capital Goods"         "Consumer Durables"    
## [274] "Public Utilities"      "Finance"               "Energy"               
## [277] "Public Utilities"      "Consumer Services"     "Capital Goods"        
## [280] "Health Care"           "Consumer Non-Durables" "Consumer Services"    
## [283] "Technology"            "Basic Industries"      "Finance"              
## [286] "Basic Industries"      "Consumer Services"     "Finance"              
## [289] "Capital Goods"         "Finance"               "Consumer Services"    
## [292] "Basic Industries"      "Finance"               "Energy"               
## [295] "Capital Goods"         "Finance"               "Energy"               
## [298] "Energy"                "Finance"               "Finance"              
## [301] "Basic Industries"      "Basic Industries"      "Miscellaneous"        
## [304] "Consumer Non-Durables" "Consumer Services"     "Health Care"          
## [307] "Health Care"           "Health Care"           "Finance"              
## [310] "Capital Goods"         "Consumer Services"     "Consumer Services"    
## [313] "Finance"               "Finance"               "Consumer Non-Durables"
## [316] "Finance"               "Finance"               "Technology"           
## [319] "Energy"                "Miscellaneous"         "Public Utilities"     
## [322] "Consumer Services"     "Basic Industries"      "Public Utilities"     
## [325] "Consumer Non-Durables" "Public Utilities"      "Energy"               
## [328] "Technology"            "Finance"               "Transportation"       
## [331] "Capital Goods"         "Consumer Services"     "Health Care"          
## [334] "Health Care"           "Basic Industries"      "Basic Industries"     
## [337] "Capital Goods"         "Energy"                "Technology"           
## [340] "Public Utilities"      "Technology"            "Public Utilities"     
## [343] "Finance"               "Technology"            "Technology"           
## [346] "Capital Goods"         "Technology"            "Energy"               
## [349] "Energy"                "Energy"                "Health Care"          
## [352] "Consumer Non-Durables" "Energy"                "Energy"               
## [355] "Public Utilities"      "Technology"            "Energy"               
## [358] "Energy"                "Finance"               "Basic Industries"     
## [361] "Basic Industries"      "Public Utilities"      "Basic Industries"     
## [364] "Finance"               "Consumer Services"     "Finance"              
## [367] "Finance"               "Public Utilities"      "Public Utilities"     
## [370] "Consumer Services"     "Health Care"           "Finance"              
## [373] "Capital Goods"         "Consumer Services"     "Finance"              
## [376] "Consumer Services"     "Public Utilities"      "Health Care"          
## [379] "Consumer Services"     "Technology"            "Basic Industries"     
## [382] "Capital Goods"         "Consumer Services"     "Finance"              
## [385] "Capital Goods"         "Finance"               "Finance"              
## [388] "Consumer Services"     "Finance"               "Technology"           
## [391] "Technology"            "Energy"                "Energy"               
## [394] "Consumer Services"     "Public Utilities"      "Technology"           
## [397] "Consumer Services"     "Finance"               "Technology"           
## [400] "Consumer Services"     "Public Utilities"      "Consumer Services"    
## [403] "Health Care"           "Technology"            "Consumer Non-Durables"
## [406] "Public Utilities"      "Basic Industries"      "Transportation"       
## [409] "Consumer Services"     "Public Utilities"      "Technology"           
## [412] "Capital Goods"         "Finance"               "Health Care"          
## [415] "Technology"            "Health Care"           "Finance"              
## [418] "Consumer Services"     "Finance"               "Energy"               
## [421] "Finance"               "Finance"               "Consumer Non-Durables"
## [424] "Technology"            "Health Care"           "Consumer Services"    
## [427] "Consumer Services"     "Public Utilities"      "Technology"           
## [430] "Energy"                "Capital Goods"         "Health Care"          
## [433] "Public Utilities"      "Public Utilities"      "Public Utilities"     
## [436] "Basic Industries"      "Consumer Services"     "Capital Goods"        
## [439] "Basic Industries"      "Finance"               "Finance"              
## [442] "Health Care"           "Finance"               "Capital Goods"        
## [445] "Consumer Services"     "Consumer Services"     "Consumer Services"    
## [448] "Finance"               "Energy"                "Miscellaneous"        
## [451] "Capital Goods"         "Capital Goods"         "Finance"              
## [454] "Technology"            "Technology"            "Consumer Non-Durables"
## [457] "Finance"               "Technology"            "Finance"              
## [460] "Consumer Services"     "Public Utilities"      "Basic Industries"     
## [463] "Basic Industries"      "Transportation"        "Transportation"       
## [466] "Capital Goods"         "Health Care"           "Health Care"          
## [469] "Consumer Non-Durables" "Basic Industries"      "Energy"               
## [472] "Technology"            "Consumer Services"     "Public Utilities"     
## [475] "Consumer Services"     "Miscellaneous"         "Public Utilities"     
## [478] "Technology"            "Consumer Services"     "Basic Industries"     
## [481] "Consumer Services"     "Finance"               "Consumer Services"    
## [484] "Consumer Services"     "Consumer Services"     "Public Utilities"     
## [487] "Public Utilities"      "Capital Goods"         "Consumer Services"    
## [490] "Public Utilities"      "Health Care"           "Finance"              
## [493] "Consumer Services"     "Basic Industries"      "Public Utilities"     
## [496] "Capital Goods"         "Finance"               "Consumer Services"    
## [499] "Basic Industries"      "Public Utilities"      "Technology"           
## [502] "Technology"            "Capital Goods"         "Consumer Services"    
## [505] "Consumer Services"     "Health Care"           "Health Care"          
## [508] "Transportation"

Based on this dataset, create a table and a bar plot that shows the number of companies per sector, in descending order

# a easier way
# nyse %>% 
#   select(sector) %>% 
#   table() %>%
#   sort(decreasing = T) %>%
#   barplot()

nyse %>%
  group_by(sector) %>% 
  mutate(company_num = count(sector)) %>%
  ggplot(aes(x=company_num, y=fct_reorder(sector, company_num))) +
  geom_bar(stat="identity") +
  labs(title = "Number of Companies in each Sector",
       x = "",
       y = "Sector")

Next, let’s choose some stocks and their ticker symbols and download some data. You MUST choose 6 different stocks from the ones listed below; You should, however, add SPY which is the SP500 ETF (Exchange Traded Fund).

# Notice the cache=TRUE argument inthe chunk options. Because getting data is time consuming, 
# cache=TRUE means that once it downloads data, the chunk will not run again next time you knit your Rmd

myStocks <- c("EBR","BBL","AEE","BCE","BRO","CAT","BUD","SPY" ) %>%
  tq_get(get  = "stock.prices",
         from = "2011-01-01",
         to   = "2021-08-31") %>%
  group_by(symbol) 

glimpse(myStocks) # examine the structure of the resulting data frame

## Rows: 21,464
## Columns: 8
## Groups: symbol [8]
## $ symbol   <chr> "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR"…
## $ date     <date> 2011-01-03, 2011-01-04, 2011-01-05, 2011-01-06, 2011-01-07, …
## $ open     <dbl> 13.9, 14.1, 14.3, 14.3, 14.0, 13.8, 13.8, 13.8, 13.9, 14.0, 1…
## $ high     <dbl> 14.0, 14.3, 14.5, 14.3, 14.0, 13.9, 13.8, 13.9, 14.1, 14.3, 1…
## $ low      <dbl> 13.9, 14.0, 14.3, 14.0, 13.8, 13.7, 13.6, 13.7, 13.8, 13.9, 1…
## $ close    <dbl> 14.0, 14.2, 14.4, 14.1, 14.0, 13.7, 13.7, 13.8, 14.0, 14.2, 1…
## $ volume   <dbl> 878300, 802900, 950300, 855800, 586000, 1020300, 1545800, 112…
## $ adjusted <dbl> 9.85, 10.00, 10.13, 9.92, 9.82, 9.63, 9.65, 9.71, 9.84, 10.03…

We can see that the dataset is 8x21,464 tibble with each row being the ohlc (open,high,low,close) and volume for a stock on a given date.

Financial performance analysis depend on returns; If I buy a stock today for 100 and I sell it tomorrow for 101.75, my one-day return, assuming no transaction costs, is 1.75%. So given the adjusted closing prices, our first step is to calculate daily and monthly returns.

#calculate daily returns
myStocks_returns_daily <- myStocks %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "daily", 
               type       = "log",
               col_rename = "daily_returns",
               cols = c(nested.col))  

#calculate monthly  returns
myStocks_returns_monthly <- myStocks %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               type       = "arithmetic",
               col_rename = "monthly_returns",
               cols = c(nested.col)) 

#calculate yearly returns
myStocks_returns_annual <- myStocks %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "yearly", 
               type       = "arithmetic",
               col_rename = "yearly_returns",
               cols = c(nested.col))

Create a table where you summarise monthly returns for each of the stocks and SPY; min, max, median, mean, SD.

glimpse(myStocks_returns_monthly)

## Rows: 1,024
## Columns: 3
## Groups: symbol [8]
## $ symbol          <chr> "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR"…
## $ date            <date> 2011-01-31, 2011-02-28, 2011-03-31, 2011-04-29, 2011-…
## $ monthly_returns <dbl> -0.0264, 0.0487, 0.0861, -0.0445, -0.0263, -0.0264, -0…

monthlystocks_summarised <- myStocks_returns_monthly %>% 
  group_by(symbol) %>%
  summarise(min_return = min(monthly_returns),
            max_return = max(monthly_returns),
            median_return = median(monthly_returns),
            mean_return = mean(monthly_returns),
            sd_return = sd(monthly_returns))

Plot a density plot, using geom_density(), for each of the stocks

ggplot(myStocks_returns_monthly, aes(x = monthly_returns)) +
  geom_density() + 
  facet_wrap(~symbol) +
  labs(
    title = "Density Plots for Monthly Returns of Stocks",
    x = "Monthly Return",
    y = "Density"
  ) +
  NULL

What can you infer from this plot? Which stock is the riskiest? The least risky?

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

The monthly returns for the flatter density plots are more dispersed whereas those with tall peaks are more concentrated. The riskiest stock is EBR and the least risky is SPY (as an ETF) due to the shape of their peaks.

Finally, make a plot that shows the expected monthly return (mean) of a stock on the Y axis and the risk (standard deviation) in the X-axis. Please use ggrepel::geom_text_repel() to label each stock

monthlystocks_summarised%>%
  ggplot(aes(y = mean_return, x=sd_return)) +
  geom_point() + 
  ggrepel::geom_text_repel(aes(label = symbol)) +
  NULL

What can you infer from this plot? Are there any stocks which, while being riskier, do not have a higher expected return?

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

EBR is the most risky as it has the highest standard deviation of returns. EBR has the highest sd but also a high expected return. AEE, CAT and SPY also have high expected returns with less standard deviation. BBL and BUD have lower expected returns and higher standard deviations meaning they are riskier and do not have high expected returns. SPY produces a good return with lower risk.

4 On your own: IBM HR Analytics

For this task, you will analyse a data set on Human Resource Analytics. The IBM HR Analytics Employee Attrition & Performance data set is a fictional data set created by IBM data scientists. Among other things, the data set includes employees’ income, their distance from work, their position in the company, their level of education, etc. A full description can be found on the website.

First let us load the data

hr_dataset <- read_csv(here::here("data", "datasets_1067_1925_WA_Fn-UseC_-HR-Employee-Attrition.csv"))
glimpse(hr_dataset)

## Rows: 1,470
## Columns: 35
## $ Age                      <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition                <chr> "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ BusinessTravel           <chr> "Travel_Rarely", "Travel_Frequently", "Travel…
## $ DailyRate                <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,…
## $ Department               <chr> "Sales", "Research & Development", "Research …
## $ DistanceFromHome         <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, …
## $ Education                <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, …
## $ EducationField           <chr> "Life Sciences", "Life Sciences", "Other", "L…
## $ EmployeeCount            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ EmployeeNumber           <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,…
## $ EnvironmentSatisfaction  <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, …
## $ Gender                   <chr> "Female", "Male", "Male", "Female", "Male", "…
## $ HourlyRate               <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4…
## $ JobInvolvement           <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, …
## $ JobLevel                 <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, …
## $ JobRole                  <chr> "Sales Executive", "Research Scientist", "Lab…
## $ JobSatisfaction          <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, …
## $ MaritalStatus            <chr> "Single", "Married", "Single", "Married", "Ma…
## $ MonthlyIncome            <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269…
## $ MonthlyRate              <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964…
## $ NumCompaniesWorked       <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, …
## $ Over18                   <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", …
## $ OverTime                 <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",…
## $ PercentSalaryHike        <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1…
## $ PerformanceRating        <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, …
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, …
## $ StandardHours            <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8…
## $ StockOptionLevel         <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, …
## $ TotalWorkingYears        <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3…
## $ TrainingTimesLastYear    <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, …
## $ WorkLifeBalance          <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, …
## $ YearsAtCompany           <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,…
## $ YearsInCurrentRole       <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, …
## $ YearsSinceLastPromotion  <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, …
## $ YearsWithCurrManager     <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, …

I am going to clean the data set, as variable names are in capital letters, some variables are not really necessary, and some variables, e.g., education are given as a number rather than a more useful description

hr_cleaned <- hr_dataset %>% 
  clean_names() %>% 
  mutate(
    education = case_when(
      education == 1 ~ "Below College",
      education == 2 ~ "College",
      education == 3 ~ "Bachelor",
      education == 4 ~ "Master",
      education == 5 ~ "Doctor"
    ),
    environment_satisfaction = case_when(
      environment_satisfaction == 1 ~ "Low",
      environment_satisfaction == 2 ~ "Medium",
      environment_satisfaction == 3 ~ "High",
      environment_satisfaction == 4 ~ "Very High"
    ),
    job_satisfaction = case_when(
      job_satisfaction == 1 ~ "Low",
      job_satisfaction == 2 ~ "Medium",
      job_satisfaction == 3 ~ "High",
      job_satisfaction == 4 ~ "Very High"
    ),
    performance_rating = case_when(
      performance_rating == 1 ~ "Low",
      performance_rating == 2 ~ "Good",
      performance_rating == 3 ~ "Excellent",
      performance_rating == 4 ~ "Outstanding"
    ),
    work_life_balance = case_when(
      work_life_balance == 1 ~ "Bad",
      work_life_balance == 2 ~ "Good",
      work_life_balance == 3 ~ "Better",
      work_life_balance == 4 ~ "Best"
    )
  ) %>% 
  select(age, attrition, daily_rate, department,
         distance_from_home, education,
         gender, job_role,environment_satisfaction,
         job_satisfaction, marital_status,
         monthly_income, num_companies_worked, percent_salary_hike,
         performance_rating, total_working_years,
         work_life_balance, years_at_company,
         years_since_last_promotion)

glimpse(hr_cleaned)

## Rows: 1,470
## Columns: 19
## $ age                        <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35,…
## $ attrition                  <chr> "Yes", "No", "Yes", "No", "No", "No", "No",…
## $ daily_rate                 <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 135…
## $ department                 <chr> "Sales", "Research & Development", "Researc…
## $ distance_from_home         <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26…
## $ education                  <chr> "College", "Below College", "College", "Mas…
## $ gender                     <chr> "Female", "Male", "Male", "Female", "Male",…
## $ job_role                   <chr> "Sales Executive", "Research Scientist", "L…
## $ environment_satisfaction   <chr> "Medium", "High", "Very High", "Very High",…
## $ job_satisfaction           <chr> "Very High", "Medium", "High", "High", "Med…
## $ marital_status             <chr> "Single", "Married", "Single", "Married", "…
## $ monthly_income             <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 2…
## $ num_companies_worked       <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5…
## $ percent_salary_hike        <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13,…
## $ performance_rating         <chr> "Excellent", "Outstanding", "Excellent", "E…
## $ total_working_years        <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5,…
## $ work_life_balance          <chr> "Bad", "Better", "Better", "Better", "Bette…
## $ years_at_company           <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, …
## $ years_since_last_promotion <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0…

Produce a one-page summary describing this dataset. Here is a non-exhaustive list of questions:

1. How often do people leave the company (attrition)

# 1233 employees stay while 237 left (19.2%).
hr_cleaned %>% 
  group_by(attrition)%>% 
  summarise(count = count(attrition))

## # A tibble: 2 × 2
##   attrition count
##   <chr>     <int>
## 1 No         1233
## 2 Yes         237

# to see the how attrition rate changes with years at company
prop.table(table(hr_cleaned[,c("years_at_company","attrition")]),1)[,2]%>%
  barplot(main="Attrition Rate vs Years At Company")
abline(h=0.192,col="red") # avergae attrition rate

• As we can see from the above table only around 20% of the employees in the dataset left the company during their working years. This shows that employees do not leave that often.

2. How are age, years_at_company, monthly_income and years_since_last_promotion distributed? can you roughly guess which of these variables is closer to Normal just by looking at summary statistics?

• As we can see from the histograms from the summary statistics, the only variable that looks normally distributed is age.

skim(hr_cleaned)

Data summary

Name	hr_cleaned
Number of rows	1470
Number of columns	19
_______________________
Column type frequency:
character	10
numeric	9
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
attrition	0	1	2	3	0	2	0
department	0	1	5	22	0	3	0
education	0	1	6	13	0	5	0
gender	0	1	4	6	0	2	0
job_role	0	1	7	25	0	9	0
environment_satisfaction	0	1	3	9	0	4	0
job_satisfaction	0	1	3	9	0	4	0
marital_status	0	1	6	8	0	3	0
performance_rating	0	1	9	11	0	2	0
work_life_balance	0	1	3	6	0	4	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	0	1	36.92	9.14	18	30	36	43	60	▂▇▇▃▂
daily_rate	0	1	802.49	403.51	102	465	802	1157	1499	▇▇▇▇▇
distance_from_home	0	1	9.19	8.11	1	2	7	14	29	▇▅▂▂▂
monthly_income	0	1	6502.93	4707.96	1009	2911	4919	8379	19999	▇▅▂▁▂
num_companies_worked	0	1	2.69	2.50	0	1	2	4	9	▇▃▂▂▁
percent_salary_hike	0	1	15.21	3.66	11	12	14	18	25	▇▅▃▂▁
total_working_years	0	1	11.28	7.78	0	6	10	15	40	▇▇▂▁▁
years_at_company	0	1	7.01	6.13	0	3	5	9	40	▇▂▁▁▁
years_since_last_promotion	0	1	2.19	3.22	0	0	1	3	15	▇▁▁▁▁

3. How are job_satisfaction and work_life_balance distributed? Don’t just report counts, but express categories as % of total?

hr_cleaned %>%  
  group_by(job_satisfaction)%>% 
  summarise(countjs = n(),
            percentagejs = countjs/nrow(hr_cleaned)*100)

## # A tibble: 4 × 3
##   job_satisfaction countjs percentagejs
##   <chr>              <int>        <dbl>
## 1 High                 442         30.1
## 2 Low                  289         19.7
## 3 Medium               280         19.0
## 4 Very High            459         31.2

hr_cleaned %>%  
  group_by(work_life_balance)%>% 
  summarise(countwlb= n(),
            percentagewlb = round(countwlb/nrow(hr_cleaned)*100,2))

## # A tibble: 4 × 3
##   work_life_balance countwlb percentagewlb
##   <chr>                <int>         <dbl>
## 1 Bad                     80          5.44
## 2 Best                   153         10.4 
## 3 Better                 893         60.8 
## 4 Good                   344         23.4

• Job satisfaction is distributed quite evenly however the categories of High and Very High are more common with around 30% each. In terms of work life balance very few people have Bad or the Best work life balance. The majority of people have better work life balance.

4. Is there any relationship between monthly income and education? Monthly income and gender?

ggplot(hr_cleaned, aes(x = fct_relevel(education, 
            "Doctor", "Master", "Bachelor", 
            "College", "Below College"), y = monthly_income)) +
  geom_boxplot()+
  labs(title = "Boxplot of Monthly Income against Education",
       x = "Education",
       y = "Monthly Income")+
  NULL

ggplot(hr_cleaned, aes(x = monthly_income, y = gender)) +
  geom_boxplot()+
  labs(title = "Boxplot of Monthly Income against Gender",
       x = "Monthly Income",
       y = "Gender")+
  NULL

• As we can see from the boxplot of males and females and their monthly income, females have a higher median monthly income which could mean that the females in the dataset could be more educated. The doctors have the highest monthly median income and those with below college education have the least. Doctors also have the highest variability in the IQR. College has the most outliers due to its low standard deviation.

5. Plot a boxplot of income vs job role. Make sure the highest-paid job roles appear first.

ggplot(hr_cleaned, aes(x=fct_reorder(job_role,-monthly_income), y=monthly_income)) +
  geom_boxplot() +
  labs(title = "Boxplot of Monthly Income against Job Role",
       x = "Job Role",
       y = "Monthly Income")+
  NULL

6. Calculate and plot a bar chart of the mean (or median?) income by education level.

hr_cleaned %>% 
  group_by(education) %>%
  summarise(medianinc = median(monthly_income),
            meaninc = mean(monthly_income)) %>% 
  ggplot(aes(x = fct_relevel(education, 
            "Doctor", "Master", "Bachelor", 
            "College", "Below College"),
            y=meaninc)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Monthly Income of Each Education Level",
       x = "Education",
       y = "Average Monthly Income")+
  NULL

• As we can see the medians and means differ greatly, this is due to the outliers in the dataset with individuals with abnormally high monthly income levels.

7. Plot the distribution of income by education level. Use a facet_wrap and a theme from ggthemes

hr_cleaned %>% 
  ggplot(aes(x=monthly_income)) +
  geom_histogram(bins=30)+
  facet_wrap(~fct_relevel(education, 
            "Doctor", "Master", "Bachelor", 
            "College", "Below College")) +
  theme_wsj() +
  NULL

8. Plot income vs age, faceted by job_role

hr_cleaned %>% 
  ggplot(aes(y=monthly_income, x=age)) +
  geom_point() +
  geom_smooth(method="lm")+
  facet_wrap(~job_role) +
  theme_wsj() +
  NULL

5 Challenge 1: Replicating a chart

The purpose of this exercise is to reproduce a plot using your dplyr and ggplot2 skills. Read the article The Racial Factor: There’s 77 Counties Which Are Deep Blue But Also Low-Vaxx. Guess What They Have In Common? and have a look at the attached figure.

You dont have to worry about the blue-red backgound and don’t worry about replicating it exactly, try and see how far you can get. You’re encouraged to work together if you want to and exchange tips/tricks you figured out– and even though the figure in the original article is from early July 2021, you can use the most recent data.

Some hints to get you started:

1. To get vaccination by county, we will use data from the CDC
2. You need to get County Presidential Election Returns 2000-2020
3. Finally, you also need an estimate of the population of each county

# 3154 unique fips code in election2020_results
length(unique(election2020_results$fips))

## [1] 3154

# check unique values of candidate names
unique(election2020_results$candidate)

## [1] "JOSEPH R BIDEN JR" "OTHER"             "DONALD J TRUMP"   
## [4] "JO JORGENSEN"

data <- election2020_results %>% 
  mutate(votes_percentage = candidatevotes/totalvotes) %>% # calculate percentage
  filter(candidate=="DONALD J TRUMP", mode=="TOTAL") %>% # we only need Trump votes 
  inner_join(vaccinations,by = "fips") %>% # inner join with vaccinations data
  inner_join(population,by = "fips") # inner join with population data


# generate graph below
# install.packages("ggpubr") # to show equation easily
library(ggpubr)
# calculate actual vaccination percentage
actual = sum(data$series_complete_yes)/sum(data$pop_estimate_2019)
data %>% 
  # filter out 0% vaccinated points
  filter(series_complete_pop_pct>0) %>%
  ggplot() +
  geom_point(aes(x=votes_percentage,y=series_complete_pop_pct/100,size=pop_estimate_2019),shape=21,fill="light blue")+
  
  # scale circle size
  scale_size(range = c(0, 20)) + 
  
  # add points in the center of circles
  geom_point(aes(x=votes_percentage,y=series_complete_pop_pct/100),size=0.5)+ 
  
  # add regression line
  geom_smooth(aes(x=votes_percentage,y=series_complete_pop_pct/100),
              method="lm",linetype="dotted",colour="blue",se=FALSE)+ 
  
  # add the equation of regression line
  stat_regline_equation(aes(x=votes_percentage,y=series_complete_pop_pct/100),
                        label.y = 0.1,colour="red",fontface = "bold.italic") + 
  
  # add r square
  stat_cor(aes(x=votes_percentage,y=series_complete_pop_pct/100,label = paste(..rr.label..)),
           label.y = 0.05,colour="red")+ 
  
  # add date
  geom_text(aes(x=0.3, y=0.07,label = "09/03/2021", 
                fontface = "bold.italic"),colour="red")+ 
  
  # add horizonal lines below
  geom_hline(aes(yintercept=0.85), linetype=2) + # herd immunity line
  geom_text(aes(x=0, y=0.85, label = "Herd Immunity threshold (?)", 
                vjust=-1, hjust=0, fontface = "bold.italic"),colour="blue") +
  geom_hline(aes(yintercept=0.539), linetype=2) + # Target line
  geom_text(aes(x=0, y=0.539,label = "TARGET: 53.9%", 
                vjust=-1,hjust=0, fontface = "bold.italic"),colour="blue") +
  geom_hline(aes(yintercept=actual), linetype=2) + # actual line
  geom_text(aes(x=0, y=actual, label = paste("ACTUAL: ", round(actual*100,1),"%"),
                vjust=-1,hjust=0,fontface = "bold.italic"),
            colour="blue") +
  
  # adjuct grid lines
  scale_x_continuous(label=scales::percent_format(accuracy = 1),
                     breaks=seq(0,1,0.05),limits = c(0,1))+
  scale_y_continuous(label=scales::percent_format(accuracy = 1),
                     breaks=seq(0,1,0.05),limits = c(0,1))+
  geom_text(aes(x=0.5,y=1, label = "EACH U.S. COUNTY",vjust=0.5,hjust=0.5,
                family="mono",fontface = "bold"),
            size=5)+
  labs(title = "COVID-19 VACCINATION LEVELS OUT OF TOTAL POPULATION BY COUNTY",
       x = "2020 Trump Vote %",
       y = "% of Total Population Vaccinated")+
  # remove the legend
  theme(legend.position = "none") +
  NULL

6 Challenge 2: Opinion polls for the 2021 German elections

The Guardian newspaper has an election poll tracker for the upcoming German election. The list of the opinion polls since Jan 2021 can be found at Wikipedia and your task is to reproduce the graph similar to the one produced by the Guardian.

The following code will scrape the wikipedia page and import the table in a dataframe.

url <- "https://en.wikipedia.org/wiki/Opinion_polling_for_the_2021_German_federal_election"
# https://www.economist.com/graphic-detail/who-will-succeed-angela-merkel
# https://www.theguardian.com/world/2021/jun/21/german-election-poll-tracker-who-will-be-the-next-chancellor


# get tables that exist on wikipedia page 
tables <- url %>% 
  read_html() %>% 
  html_nodes(css="table")


# parse HTML tables into a dataframe called polls 
# Use purr::map() to create a list of all tables in URL
polls <- map(tables, . %>% 
             html_table(fill=TRUE)%>% 
             janitor::clean_names())


# list of opinion polls
german_election_polls <- polls[[1]] %>% # the first table on the page contains the list of all opinions polls
  slice(2:(n()-1)) %>%  # drop the first row, as it contains again the variable names and last row that contains 2017 results
  mutate(
         # polls are shown to run from-to, e.g. 9-13 Aug 2021. We keep the last date, 13 Aug here, as the poll date
         # and we extract it by picking the last 11 characters from that field
         end_date = str_sub(fieldwork_date, -11),
         
         # end_date is still a string, so we convert it into a date object using lubridate::dmy()
         end_date = dmy(end_date),
         
         # we also get the month and week number from the date, if we want to do analysis by month- week, etc.
         month = month(end_date),
         week = isoweek(end_date)
         )

stack(german_election_polls)

##                                values            ind
## 1             Forschungsgruppe Wahlen   polling_firm
## 2                     Infratest dimap   polling_firm
## 3                   Wahlkreisprognose   polling_firm
## 4                              YouGov   polling_firm
## 5                              Kantar   polling_firm
## 6                                INSA   polling_firm
## 7                               Forsa   polling_firm
## 8                               Ipsos   polling_firm
## 9                                INSA   polling_firm
## 10            Forschungsgruppe Wahlen   polling_firm
## 11                         Allensbach   polling_firm
## 12                              Civey   polling_firm
## 13                             YouGov   polling_firm
## 14                             Kantar   polling_firm
## 15                               INSA   polling_firm
## 16                              Forsa   polling_firm
## 17                               INSA   polling_firm
## 18                    Infratest dimap   polling_firm
## 19                     Trend Research   polling_firm
## 20                              Civey   polling_firm
## 21                  Wahlkreisprognose   polling_firm
## 22                             Kantar   polling_firm
## 23                         Allensbach   polling_firm
## 24                               INSA   polling_firm
## 25                              Forsa   polling_firm
## 26                               INSA   polling_firm
## 27            Forschungsgruppe Wahlen   polling_firm
## 28                              Civey   polling_firm
## 29                             Kantar   polling_firm
## 30                               INSA   polling_firm
## 31                              Forsa   polling_firm
## 32                               INSA   polling_firm
## 33                    Infratest dimap   polling_firm
## 34                              Civey   polling_firm
## 35                             Kantar   polling_firm
## 36                               INSA   polling_firm
## 37                              Forsa   polling_firm
## 38                              Ipsos   polling_firm
## 39                               INSA   polling_firm
## 40            Forschungsgruppe Wahlen   polling_firm
## 41                              Civey   polling_firm
## 42                                GMS   polling_firm
## 43                             Kantar   polling_firm
## 44                             YouGov   polling_firm
## 45                               INSA   polling_firm
## 46                              Forsa   polling_firm
## 47                               INSA   polling_firm
## 48                         Allensbach   polling_firm
## 49                    Infratest dimap   polling_firm
## 50                              Civey   polling_firm
## 51                             Kantar   polling_firm
## 52                               INSA   polling_firm
## 53                              Forsa   polling_firm
## 54                               INSA   polling_firm
## 55            Forschungsgruppe Wahlen   polling_firm
## 56                              Civey   polling_firm
## 57   Allensbach (intermediate result)   polling_firm
## 58                             Kantar   polling_firm
## 59                               INSA   polling_firm
## 60                              Forsa   polling_firm
## 61                               INSA   polling_firm
## 62                              Civey   polling_firm
## 63                             Kantar   polling_firm
## 64                               INSA   polling_firm
## 65                              Forsa   polling_firm
## 66                               INSA   polling_firm
## 67                    Infratest dimap   polling_firm
## 68                              Civey   polling_firm
## 69                             Kantar   polling_firm
## 70                             YouGov   polling_firm
## 71                               INSA   polling_firm
## 72                              Forsa   polling_firm
## 73                              Ipsos   polling_firm
## 74                               INSA   polling_firm
## 75            Forschungsgruppe Wahlen   polling_firm
## 76                    Infratest dimap   polling_firm
## 77                              Civey   polling_firm
## 78                             Kantar   polling_firm
## 79                              Forsa   polling_firm
## 80                               INSA   polling_firm
## 81                                GMS   polling_firm
## 82                              Forsa   polling_firm
## 83                               INSA   polling_firm
## 84                              Civey   polling_firm
## 85                             Kantar   polling_firm
## 86                               INSA   polling_firm
## 87                              Forsa   polling_firm
## 88                               INSA   polling_firm
## 89       Redfield & Wilton Strategies   polling_firm
## 90                    Infratest dimap   polling_firm
## 91            Forschungsgruppe Wahlen   polling_firm
## 92                              Civey   polling_firm
## 93                         Allensbach   polling_firm
## 94                             Kantar   polling_firm
## 95                               INSA   polling_firm
## 96                              Forsa   polling_firm
## 97                               INSA   polling_firm
## 98                              Civey   polling_firm
## 99                             Kantar   polling_firm
## 100                              INSA   polling_firm
## 101                             Forsa   polling_firm
## 102                              INSA   polling_firm
## 103                              INSA   polling_firm
## 104                            Kantar   polling_firm
## 105                             Civey   polling_firm
## 106                             Ipsos   polling_firm
## 107                            YouGov   polling_firm
## 108                             Forsa   polling_firm
## 109                              INSA   polling_firm
## 110           Forschungsgruppe Wahlen   polling_firm
## 111                             Civey   polling_firm
## 112                            Kantar   polling_firm
## 113                              INSA   polling_firm
## 114                             Forsa   polling_firm
## 115                               GMS   polling_firm
## 116                              INSA   polling_firm
## 117                        Allensbach   polling_firm
## 118                   Infratest dimap   polling_firm
## 119                            Kantar   polling_firm
## 120                              INSA   polling_firm
## 121                             Forsa   polling_firm
## 122      Redfield & Wilton Strategies   polling_firm
## 123           Forschungsgruppe Wahlen   polling_firm
## 124                   Infratest dimap   polling_firm
## 125                            Kantar   polling_firm
## 126                             Civey   polling_firm
## 127                              INSA   polling_firm
## 128                             Forsa   polling_firm
## 129                            Kantar   polling_firm
## 130                              INSA   polling_firm
## 131                            YouGov   polling_firm
## 132                             Forsa   polling_firm
## 133                             Civey   polling_firm
## 134        INSA (intermediate result)   polling_firm
## 135                            Kantar   polling_firm
## 136                             Civey   polling_firm
## 137       Forsa (intermediate result)   polling_firm
## 138                              INSA   polling_firm
## 139                              INSA   polling_firm
## 140                             Forsa   polling_firm
## 141                              INSA   polling_firm
## 142           Forschungsgruppe Wahlen   polling_firm
## 143                        Allensbach   polling_firm
## 144                   Infratest dimap   polling_firm
## 145                            Kantar   polling_firm
## 146                              INSA   polling_firm
## 147                             Forsa   polling_firm
## 148                            Kantar   polling_firm
## 149                              INSA   polling_firm
## 150                             Forsa   polling_firm
## 151                            Kantar   polling_firm
## 152                   Infratest dimap   polling_firm
## 153                              INSA   polling_firm
## 154                            YouGov   polling_firm
## 155                               GMS   polling_firm
## 156                             Forsa   polling_firm
## 157           Forschungsgruppe Wahlen   polling_firm
## 158                            Kantar   polling_firm
## 159                             Civey   polling_firm
## 160                              INSA   polling_firm
## 161                             Forsa   polling_firm
## 162                        Allensbach   polling_firm
## 163        INSA (intermediate result)   polling_firm
## 164                   Infratest dimap   polling_firm
## 165                            Kantar   polling_firm
## 166                             Civey   polling_firm
## 167                              INSA   polling_firm
## 168                             Forsa   polling_firm
## 169                            Kantar   polling_firm
## 170                              INSA   polling_firm
## 171                             Forsa   polling_firm
## 172                            Kantar   polling_firm
## 173                   Infratest dimap   polling_firm
## 174                              INSA   polling_firm
## 175                             Forsa   polling_firm
## 176           Forschungsgruppe Wahlen   polling_firm
## 177                            Kantar   polling_firm
## 178                              INSA   polling_firm
## 179                            YouGov   polling_firm
## 180                             Forsa   polling_firm
## 181                   Infratest dimap   polling_firm
## 182                            Kantar   polling_firm
## 183                        Allensbach   polling_firm
## 184                              INSA   polling_firm
## 185                               GMS   polling_firm
## 186                             Forsa   polling_firm
## 187                            Kantar   polling_firm
## 188                              INSA   polling_firm
## 189                             Forsa   polling_firm
## 190                   Infratest dimap   polling_firm
## 191                            Kantar   polling_firm
## 192                            YouGov   polling_firm
## 193                              INSA   polling_firm
## 194                             Forsa   polling_firm
## 195           Forschungsgruppe Wahlen   polling_firm
## 196                            Kantar   polling_firm
## 197                              INSA   polling_firm
## 198                             Forsa   polling_firm
## 199                   Infratest dimap   polling_firm
## 200                            Kantar   polling_firm
## 201                        Allensbach   polling_firm
## 202        INSA (intermediate result)   polling_firm
## 203                              INSA   polling_firm
## 204                             Forsa   polling_firm
## 205                             Forsa   polling_firm
## 206           Forschungsgruppe Wahlen   polling_firm
## 207                            Kantar   polling_firm
## 208                              INSA   polling_firm
## 209                             Forsa   polling_firm
## 210                   Infratest dimap   polling_firm
## 211                            YouGov   polling_firm
## 212                              INSA   polling_firm
## 213                               GMS   polling_firm
## 214                 31 Aug–2 Sep 2021 fieldwork_date
## 215                 30 Aug–1 Sep 2021 fieldwork_date
## 216                    30–31 Aug 2021 fieldwork_date
## 217                    27–31 Aug 2021 fieldwork_date
## 218                    25–31 Aug 2021 fieldwork_date
## 219                    27–30 Aug 2021 fieldwork_date
## 220                    24–30 Aug 2021 fieldwork_date
## 221                    28–29 Aug 2021 fieldwork_date
## 222                    23–27 Aug 2021 fieldwork_date
## 223                    24–26 Aug 2021 fieldwork_date
## 224                    18–26 Aug 2021 fieldwork_date
## 225                    18–25 Aug 2021 fieldwork_date
## 226                    20–24 Aug 2021 fieldwork_date
## 227                    18–24 Aug 2021 fieldwork_date
## 228                    20–23 Aug 2021 fieldwork_date
## 229                    16–23 Aug 2021 fieldwork_date
## 230                    16–20 Aug 2021 fieldwork_date
## 231                    17–18 Aug 2021 fieldwork_date
## 232                    12–18 Aug 2021 fieldwork_date
## 233                    11–18 Aug 2021 fieldwork_date
## 234                    14–17 Aug 2021 fieldwork_date
## 235                    11–17 Aug 2021 fieldwork_date
## 236                     5–17 Aug 2021 fieldwork_date
## 237                    13–16 Aug 2021 fieldwork_date
## 238                    10–16 Aug 2021 fieldwork_date
## 239                     9–13 Aug 2021 fieldwork_date
## 240                    10–12 Aug 2021 fieldwork_date
## 241                     4–11 Aug 2021 fieldwork_date
## 242                     4–10 Aug 2021 fieldwork_date
## 243                      6–9 Aug 2021 fieldwork_date
## 244                      3–9 Aug 2021 fieldwork_date
## 245                      2–6 Aug 2021 fieldwork_date
## 246                      2–4 Aug 2021 fieldwork_date
## 247                 28 Jul–4 Aug 2021 fieldwork_date
## 248                 28 Jul–3 Aug 2021 fieldwork_date
## 249                 30 Jul–2 Aug 2021 fieldwork_date
## 250                 27 Jul–2 Aug 2021 fieldwork_date
## 251                    21–31 Jul 2021 fieldwork_date
## 252                    26–30 Jul 2021 fieldwork_date
## 253                    27–29 Jul 2021 fieldwork_date
## 254                    21–28 Jul 2021 fieldwork_date
## 255                    21–27 Jul 2021 fieldwork_date
## 256                    20–27 Jul 2021 fieldwork_date
## 257                    23–26 Jul 2021 fieldwork_date
## 258                    23–26 Jul 2021 fieldwork_date
## 259                    20–26 Jul 2021 fieldwork_date
## 260                    19–23 Jul 2021 fieldwork_date
## 261                     3–22 Jul 2021 fieldwork_date
## 262                    20–21 Jul 2021 fieldwork_date
## 263                    14–21 Jul 2021 fieldwork_date
## 264                    14–20 Jul 2021 fieldwork_date
## 265                    16–19 Jul 2021 fieldwork_date
## 266                    13–19 Jul 2021 fieldwork_date
## 267                    12–16 Jul 2021 fieldwork_date
## 268                    13–15 Jul 2021 fieldwork_date
## 269                     7–14 Jul 2021 fieldwork_date
## 270                     3–14 Jul 2021 fieldwork_date
## 271                     7–13 Jul 2021 fieldwork_date
## 272                     9–12 Jul 2021 fieldwork_date
## 273                     6–12 Jul 2021 fieldwork_date
## 274                      5–9 Jul 2021 fieldwork_date
## 275                 30 Jun–7 Jul 2021 fieldwork_date
## 276                 29 Jun–6 Jul 2021 fieldwork_date
## 277                      2–5 Jul 2021 fieldwork_date
## 278                 29 Jun–5 Jul 2021 fieldwork_date
## 279                 28 Jun–2 Jul 2021 fieldwork_date
## 280                    28–30 Jun 2021 fieldwork_date
## 281                    23–30 Jun 2021 fieldwork_date
## 282                    23–29 Jun 2021 fieldwork_date
## 283                    25–28 Jun 2021 fieldwork_date
## 284                    25–28 Jun 2021 fieldwork_date
## 285                    22–28 Jun 2021 fieldwork_date
## 286                    21–27 Jun 2021 fieldwork_date
## 287                    21–25 Jun 2021 fieldwork_date
## 288                    22–24 Jun 2021 fieldwork_date
## 289                    22–23 Jun 2021 fieldwork_date
## 290                    16–23 Jun 2021 fieldwork_date
## 291                    15–22 Jun 2021 fieldwork_date
## 292                    15–21 Jun 2021 fieldwork_date
## 293                    18–21 Jun 2021 fieldwork_date
## 294                    16–21 Jun 2021 fieldwork_date
## 295                    15–21 Jun 2021 fieldwork_date
## 296                    14–18 Jun 2021 fieldwork_date
## 297                     9–16 Jun 2021 fieldwork_date
## 298                     9–15 Jun 2021 fieldwork_date
## 299                    11–14 Jun 2021 fieldwork_date
## 300                     8–14 Jun 2021 fieldwork_date
## 301                     7–11 Jun 2021 fieldwork_date
## 302                      8–9 Jun 2021 fieldwork_date
## 303                      7–9 Jun 2021 fieldwork_date
## 304                      7–9 Jun 2021 fieldwork_date
## 305                      2–9 Jun 2021 fieldwork_date
## 306                 28 May–9 Jun 2021 fieldwork_date
## 307                      2–8 Jun 2021 fieldwork_date
## 308                      4–7 Jun 2021 fieldwork_date
## 309                      1–7 Jun 2021 fieldwork_date
## 310                 31 May–4 Jun 2021 fieldwork_date
## 311                 26 May–2 Jun 2021 fieldwork_date
## 312                 26 May–1 Jun 2021 fieldwork_date
## 313                    28–31 May 2021 fieldwork_date
## 314                    25–31 May 2021 fieldwork_date
## 315                    25–28 May 2021 fieldwork_date
## 316                    21–25 May 2021 fieldwork_date
## 317                    19–25 May 2021 fieldwork_date
## 318                    19–25 May 2021 fieldwork_date
## 319                    18–25 May 2021 fieldwork_date
## 320                    21–24 May 2021 fieldwork_date
## 321                    18–21 May 2021 fieldwork_date
## 322                    17–21 May 2021 fieldwork_date
## 323                    18–20 May 2021 fieldwork_date
## 324                    12–19 May 2021 fieldwork_date
## 325                    11–18 May 2021 fieldwork_date
## 326                    14–17 May 2021 fieldwork_date
## 327                    11–17 May 2021 fieldwork_date
## 328                    11–17 May 2021 fieldwork_date
## 329                    10–14 May 2021 fieldwork_date
## 330                     1–13 May 2021 fieldwork_date
## 331                    10–11 May 2021 fieldwork_date
## 332                     6–11 May 2021 fieldwork_date
## 333                     7–10 May 2021 fieldwork_date
## 334                     4–10 May 2021 fieldwork_date
## 335                      5–6 May 2021 fieldwork_date
## 336                      4–6 May 2021 fieldwork_date
## 337                      3–5 May 2021 fieldwork_date
## 338                 29 Apr–5 May 2021 fieldwork_date
## 339                 28 Apr–5 May 2021 fieldwork_date
## 340                 30 Apr–3 May 2021 fieldwork_date
## 341                 27 Apr–3 May 2021 fieldwork_date
## 342                    22–28 Apr 2021 fieldwork_date
## 343                    23–26 Apr 2021 fieldwork_date
## 344                    23–26 Apr 2021 fieldwork_date
## 345                    20–26 Apr 2021 fieldwork_date
## 346                    19–26 Apr 2021 fieldwork_date
## 347                       23 Apr 2021 fieldwork_date
## 348                    15–21 Apr 2021 fieldwork_date
## 349                    14–21 Apr 2021 fieldwork_date
## 350                       20 Apr 2021 fieldwork_date
## 351                       20 Apr 2021 fieldwork_date
## 352                    16–19 Apr 2021 fieldwork_date
## 353                    13–16 Apr 2021 fieldwork_date
## 354                       15 Apr 2021 fieldwork_date
## 355                    13–15 Apr 2021 fieldwork_date
## 356                     6–15 Apr 2021 fieldwork_date
## 357                    13–14 Apr 2021 fieldwork_date
## 358                     8–14 Apr 2021 fieldwork_date
## 359                     9–12 Apr 2021 fieldwork_date
## 360                     6–12 Apr 2021 fieldwork_date
## 361                 31 Mar–7 Apr 2021 fieldwork_date
## 362                 30 Mar–1 Apr 2021 fieldwork_date
## 363                 30 Mar–1 Apr 2021 fieldwork_date
## 364                    25–31 Mar 2021 fieldwork_date
## 365                    29–30 Mar 2021 fieldwork_date
## 366                    26–29 Mar 2021 fieldwork_date
## 367                    25–29 Mar 2021 fieldwork_date
## 368                    24–29 Mar 2021 fieldwork_date
## 369                    23–29 Mar 2021 fieldwork_date
## 370                    23–25 Mar 2021 fieldwork_date
## 371                    18–24 Mar 2021 fieldwork_date
## 372                    17–24 Mar 2021 fieldwork_date
## 373                    19–22 Mar 2021 fieldwork_date
## 374                    16–22 Mar 2021 fieldwork_date
## 375                     8–21 Mar 2021 fieldwork_date
## 376                       19 Mar 2021 fieldwork_date
## 377                    15–17 Mar 2021 fieldwork_date
## 378                    11–17 Mar 2021 fieldwork_date
## 379                    10–17 Mar 2021 fieldwork_date
## 380                    12–15 Mar 2021 fieldwork_date
## 381                     9–15 Mar 2021 fieldwork_date
## 382                     4–10 Mar 2021 fieldwork_date
## 383                      5–8 Mar 2021 fieldwork_date
## 384                      2–8 Mar 2021 fieldwork_date
## 385                 25 Feb–3 Mar 2021 fieldwork_date
## 386                      1–2 Mar 2021 fieldwork_date
## 387                 26 Feb–1 Mar 2021 fieldwork_date
## 388                 25 Feb–1 Mar 2021 fieldwork_date
## 389                    23–25 Feb 2021 fieldwork_date
## 390                    18–24 Feb 2021 fieldwork_date
## 391                    19–22 Feb 2021 fieldwork_date
## 392                    18–22 Feb 2021 fieldwork_date
## 393                    16–22 Feb 2021 fieldwork_date
## 394                    15–17 Feb 2021 fieldwork_date
## 395                    11–17 Feb 2021 fieldwork_date
## 396                     4–17 Feb 2021 fieldwork_date
## 397                    12–15 Feb 2021 fieldwork_date
## 398                    10–15 Feb 2021 fieldwork_date
## 399                     9–15 Feb 2021 fieldwork_date
## 400                     4–10 Feb 2021 fieldwork_date
## 401                      5–8 Feb 2021 fieldwork_date
## 402                      2–8 Feb 2021 fieldwork_date
## 403                      1–3 Feb 2021 fieldwork_date
## 404                 28 Jan–3 Feb 2021 fieldwork_date
## 405                 29 Jan–1 Feb 2021 fieldwork_date
## 406                 29 Jan–1 Feb 2021 fieldwork_date
## 407                 26 Jan–1 Feb 2021 fieldwork_date
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## 410                    22–25 Jan 2021 fieldwork_date
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## 413                    14–20 Jan 2021 fieldwork_date
## 414                    10–20 Jan 2021 fieldwork_date
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## 416                    15–18 Jan 2021 fieldwork_date
## 417                    16–17 Jan 2021 fieldwork_date
## 418                    11–15 Jan 2021 fieldwork_date
## 419                    12–14 Jan 2021 fieldwork_date
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## 422                      4–8 Jan 2021 fieldwork_date
## 423                      4–6 Jan 2021 fieldwork_date
## 424            30 Dec 2020–5 Jan 2021 fieldwork_date
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## 426            29 Dec 2020–4 Jan 2021 fieldwork_date
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## 502                             1,217     samplesize
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## 531                            10,016     samplesize
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## 539                             2,023     samplesize
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## 543                             1,027     samplesize
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## 552                            10,028     samplesize
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## 640                                29            abs
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## 853                                22          union
## 854                                20          union
## 855                              19.5          union
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## 1039                               35          union
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## 1042                               34          union
## 1043                               36          union
## 1044                               36          union
## 1045                             36.5          union
## 1046                               37          union
## 1047                               37          union
## 1048                               36          union
## 1049                               35          union
## 1050                               37          union
## 1051                               34          union
## 1052                               35          union
## 1053                               37          union
## 1054                               35          union
## 1055                             35.5          union
## 1056                               35          union
## 1057                               35          union
## 1058                               37          union
## 1059                               36          union
## 1060                               36          union
## 1061                               36          union
## 1062                               35          union
## 1063                               36          union
## 1064                               36          union
## 1065                               37          union
## 1066                               25            spd
## 1067                               25            spd
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## 1069                               25            spd
## 1070                               25            spd
## 1071                               25            spd
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## 1073                               25            spd
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## 1075                               22            spd
## 1076                               24            spd
## 1077                               22            spd
## 1078                               24            spd
## 1079                               23            spd
## 1080                               23            spd
## 1081                               23            spd
## 1082                               22            spd
## 1083                               21            spd
## 1084                               21            spd
## 1085                               19            spd
## 1086                               22            spd
## 1087                               21            spd
## 1088                             19.5            spd
## 1089                               20            spd
## 1090                               21            spd
## 1091                               20            spd
## 1092                               19            spd
## 1093                               18            spd
## 1094                               19            spd
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## 1096                               19            spd
## 1097                               18            spd
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## 1100                               18            spd
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## 1189                               14            spd
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## 1200                               13            spd
## 1201                               15            spd
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## 1204                               16            spd
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## 1210                               15            spd
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## 1212                               15            spd
## 1213                               15            spd
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## 1220                               16            spd
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## 1222                               15            spd
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## 1224                               16            spd
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## 1226                               16            spd
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## 1228                               18            spd
## 1229                               17            spd
## 1230                               17            spd
## 1231                               17            spd
## 1232                               17            spd
## 1233                               16            spd
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## 1236                               16            spd
## 1237                               16            spd
## 1238                               16            spd
## 1239                               17            spd
## 1240                               16            spd
## 1241                               16            spd
## 1242                               16            spd
## 1243                               16            spd
## 1244                               16            spd
## 1245                               16            spd
## 1246                               16            spd
## 1247                               16            spd
## 1248                               15            spd
## 1249                               17            spd
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## 1251                               16            spd
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## 1255                               15            spd
## 1256                               16            spd
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## 1258                               15            spd
## 1259                               15            spd
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## 1261                               15            spd
## 1262                               16            spd
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## 1265                               15            spd
## 1266                               16            spd
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## 1269                               15            spd
## 1270                               15            spd
## 1271                               15            spd
## 1272                               15            spd
## 1273                               15            spd
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## 1275                               14            spd
## 1276                               15            spd
## 1277                               15            spd
## 1278                               16            spd
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## 1291                               11           af_d
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## 1296                               11           af_d
## 1297                               12           af_d
## 1298                               11           af_d
## 1299                               11           af_d
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## 1400                               11           af_d
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## 1442                               11           af_d
## 1443                               10           af_d
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## 1447                               11           af_d
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## 1450                               10           af_d
## 1451                               11           af_d
## 1452                               11           af_d
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## 1454                               10           af_d
## 1455                                9           af_d
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## 1457                               11           af_d
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## 1459                               11           af_d
## 1460                                9           af_d
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## 1463                                9           af_d
## 1464                                8           af_d
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## 1468                               10           af_d
## 1469                                9           af_d
## 1470                               10           af_d
## 1471                               11           af_d
## 1472                                8           af_d
## 1473                                9           af_d
## 1474                                9           af_d
## 1475                               10           af_d
## 1476                                9           af_d
## 1477                               10           af_d
## 1478                                9           af_d
## 1479                                9           af_d
## 1480                               11           af_d
## 1481                               11           af_d
## 1482                                9           af_d
## 1483                                9           af_d
## 1484                               10           af_d
## 1485                               10           af_d
## 1486                               10           af_d
## 1487                                8           af_d
## 1488                               10           af_d
## 1489                               10           af_d
## 1490                               11           af_d
## 1491                                9           af_d
## 1492                               11            fdp
## 1493                               13            fdp
## 1494                               13            fdp
## 1495                               13            fdp
## 1496                               11            fdp
## 1497                             13.5            fdp
## 1498                               12            fdp
## 1499                               11            fdp
## 1500                               13            fdp
## 1501                               10            fdp
## 1502                             10.5            fdp
## 1503                               12            fdp
## 1504                               13            fdp
## 1505                               12            fdp
## 1506                               13            fdp
## 1507                               12            fdp
## 1508                               13            fdp
## 1509                               13            fdp
## 1510                               13            fdp
## 1511                               12            fdp
## 1512                               11            fdp
## 1513                               12            fdp
## 1514                               11            fdp
## 1515                             12.5            fdp
## 1516                               12            fdp
## 1517                               12            fdp
## 1518                               11            fdp
## 1519                               12            fdp
## 1520                               12            fdp
## 1521                             12.5            fdp
## 1522                               12            fdp
## 1523                               12            fdp
## 1524                               12            fdp
## 1525                               12            fdp
## 1526                               13            fdp
## 1527                               13            fdp
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## 1535                               12            fdp
## 1536                               13            fdp
## 1537                               13            fdp
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## 1539                               12            fdp
## 1540                               12            fdp
## 1541                               11            fdp
## 1542                               12            fdp
## 1543                               12            fdp
## 1544                               12            fdp
## 1545                               12            fdp
## 1546                               10            fdp
## 1547                               11            fdp
## 1548                               12            fdp
## 1549                               11            fdp
## 1550                             12.5            fdp
## 1551                               12            fdp
## 1552                               12            fdp
## 1553                               11            fdp
## 1554                               11            fdp
## 1555                             12.5            fdp
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## 1557                               12            fdp
## 1558                               11            fdp
## 1559                               12            fdp
## 1560                               10            fdp
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## 1570                               13            fdp
## 1571                               14            fdp
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## 1573                               13            fdp
## 1574                               13            fdp
## 1575                               13            fdp
## 1576                               12            fdp
## 1577                             13.5            fdp
## 1578                               14            fdp
## 1579                               13            fdp
## 1580                               14            fdp
## 1581                               12            fdp
## 1582                               10            fdp
## 1583                               12            fdp
## 1584                               11            fdp
## 1585                               13            fdp
## 1586                             13.5            fdp
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## 1588                               12            fdp
## 1589                               12            fdp
## 1590                               13            fdp
## 1591                             13.5            fdp
## 1592                               14            fdp
## 1593                               13            fdp
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## 1596                               13            fdp
## 1597                               11            fdp
## 1598                               12            fdp
## 1599                               13            fdp
## 1600                               13            fdp
## 1601                               11            fdp
## 1602                               12            fdp
## 1603                               11            fdp
## 1604                             12.5            fdp
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## 1606                               11            fdp
## 1607                               11            fdp
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## 1623                               12            fdp
## 1624                               11            fdp
## 1625                               12            fdp
## 1626                                9            fdp
## 1627                                9            fdp
## 1628                               12            fdp
## 1629                               11            fdp
## 1630                               11            fdp
## 1631                               10            fdp
## 1632                               10            fdp
## 1633                                9            fdp
## 1634                               10            fdp
## 1635                               11            fdp
## 1636                                9            fdp
## 1637                               10            fdp
## 1638                                9            fdp
## 1639                                9            fdp
## 1640                               10            fdp
## 1641                               10            fdp
## 1642                                9            fdp
## 1643                                9            fdp
## 1644                             10.5            fdp
## 1645                               10            fdp
## 1646                               11            fdp
## 1647                               10            fdp
## 1648                                9            fdp
## 1649                               10            fdp
## 1650                                9            fdp
## 1651                             10.5            fdp
## 1652                               10            fdp
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## 1654                               10            fdp
## 1655                                9            fdp
## 1656                               10            fdp
## 1657                                9            fdp
## 1658                             10.5            fdp
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## 1660                                8            fdp
## 1661                             10.5            fdp
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## 1663                                9            fdp
## 1664                                7            fdp
## 1665                               10            fdp
## 1666                                7            fdp
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## 1677                                7            fdp
## 1678                                8            fdp
## 1679                                9            fdp
## 1680                                7            fdp
## 1681                                8            fdp
## 1682                                7            fdp
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## 1685                                6            fdp
## 1686                                6            fdp
## 1687                                7            fdp
## 1688                                8            fdp
## 1689                                7            fdp
## 1690                                7            fdp
## 1691                                7            fdp
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## 1696                                6            fdp
## 1697                                5            fdp
## 1698                                7            fdp
## 1699                              7.5            fdp
## 1700                                7            fdp
## 1701                                7            fdp
## 1702                                6            fdp
## 1703                              7.5            fdp
## 1704                                6            fdp
## 1705                                7          linke
## 1706                                6          linke
## 1707                              6.5          linke
## 1708                                8          linke
## 1709                                7          linke
## 1710                                7          linke
## 1711                                6          linke
## 1712                                7          linke
## 1713                                6          linke
## 1714                                6          linke
## 1715                                6          linke
## 1716                                7          linke
## 1717                                8          linke
## 1718                                7          linke
## 1719                                7          linke
## 1720                                6          linke
## 1721                                7          linke
## 1722                                7          linke
## 1723                                7          linke
## 1724                                7          linke
## 1725                                7          linke
## 1726                                7          linke
## 1727                              7.5          linke
## 1728                              6.5          linke
## 1729                                6          linke
## 1730                                7          linke
## 1731                                7          linke
## 1732                                7          linke
## 1733                                7          linke
## 1734                              6.5          linke
## 1735                                7          linke
## 1736                                7          linke
## 1737                                6          linke
## 1738                                6          linke
## 1739                                6          linke
## 1740                                7          linke
## 1741                                6          linke
## 1742                                7          linke
## 1743                                6          linke
## 1744                                7          linke
## 1745                                7          linke
## 1746                                7          linke
## 1747                                6          linke
## 1748                                8          linke
## 1749                                6          linke
## 1750                                7          linke
## 1751                                7          linke
## 1752                                7          linke
## 1753                                6          linke
## 1754                                6          linke
## 1755                                7          linke
## 1756                                6          linke
## 1757                                7          linke
## 1758                                7          linke
## 1759                                7          linke
## 1760                                6          linke
## 1761                              6.5          linke
## 1762                                8          linke
## 1763                                7          linke
## 1764                                7          linke
## 1765                                8          linke
## 1766                                6          linke
## 1767                                8          linke
## 1768                                7          linke
## 1769                                7          linke
## 1770                                7          linke
## 1771                                7          linke
## 1772                                6          linke
## 1773                                7          linke
## 1774                                7          linke
## 1775                              7.5          linke
## 1776                                7          linke
## 1777                                8          linke
## 1778                                7          linke
## 1779                                7          linke
## 1780                                6          linke
## 1781                                6          linke
## 1782                                7          linke
## 1783                                6          linke
## 1784                                7          linke
## 1785                                8          linke
## 1786                                6          linke
## 1787                                6          linke
## 1788                                6          linke
## 1789                                7          linke
## 1790                                6          linke
## 1791                                7          linke
## 1792                                7          linke
## 1793                               12          linke
## 1794                                7          linke
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## 1796                                6          linke
## 1797                                7          linke
## 1798                                7          linke
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## 1800                                6          linke
## 1801                                6          linke
## 1802                                6          linke
## 1803                                7          linke
## 1804                              6.5          linke
## 1805                                6          linke
## 1806                                7          linke
## 1807                              6.5          linke
## 1808                                7          linke
## 1809                                6          linke
## 1810                                9          linke
## 1811                                7          linke
## 1812                                6          linke
## 1813                                6          linke
## 1814                                7          linke
## 1815                                6          linke
## 1816                                8          linke
## 1817                              6.5          linke
## 1818                                6          linke
## 1819                                7          linke
## 1820                                7          linke
## 1821                              6.5          linke
## 1822                                7          linke
## 1823                                8          linke
## 1824                              7.5          linke
## 1825                                6          linke
## 1826                               11          linke
## 1827                                7          linke
## 1828                                6          linke
## 1829                                7          linke
## 1830                                6          linke
## 1831                                7          linke
## 1832                                6          linke
## 1833                                7          linke
## 1834                                8          linke
## 1835                                8          linke
## 1836                                7          linke
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## 1839                                7          linke
## 1840                                7          linke
## 1841                                7          linke
## 1842                                7          linke
## 1843                                7          linke
## 1844                                6          linke
## 1845                                8          linke
## 1846                                7          linke
## 1847                              7.5          linke
## 1848                                7          linke
## 1849                                8          linke
## 1850                                7          linke
## 1851                                8          linke
## 1852                                9          linke
## 1853                                7          linke
## 1854                                7          linke
## 1855                                9          linke
## 1856                                7          linke
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## 1858                                8          linke
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## 1860                                7          linke
## 1861                                7          linke
## 1862                                9          linke
## 1863                                7          linke
## 1864                                7          linke
## 1865                                8          linke
## 1866                              8.5          linke
## 1867                                8          linke
## 1868                                7          linke
## 1869                                8          linke
## 1870                                8          linke
## 1871                                8          linke
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## 1873                                8          linke
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## 1876                                9          linke
## 1877                                7          linke
## 1878                                8          linke
## 1879                                8          linke
## 1880                                7          linke
## 1881                                9          linke
## 1882                                8          linke
## 1883                                8          linke
## 1884                                7          linke
## 1885                                6          linke
## 1886                                8          linke
## 1887                                7          linke
## 1888                                9          linke
## 1889                                7          linke
## 1890                                8          linke
## 1891                                7          linke
## 1892                              8.5          linke
## 1893                                7          linke
## 1894                                6          linke
## 1895                                7          linke
## 1896                                9          linke
## 1897                              7.5          linke
## 1898                                8          linke
## 1899                                7          linke
## 1900                                8          linke
## 1901                                8          linke
## 1902                                7          linke
## 1903                                6          linke
## 1904                                8          linke
## 1905                              7.5          linke
## 1906                                8          linke
## 1907                              7.5          linke
## 1908                                8          linke
## 1909                                8          linke
## 1910                                8          linke
## 1911                                8          linke
## 1912                                8          linke
## 1913                                8          linke
## 1914                                7          linke
## 1915                                9          linke
## 1916                              7.5          linke
## 1917                                8          linke
## 1918                               17          grune
## 1919                               16          grune
## 1920                             15.5          grune
## 1921                               15          grune
## 1922                               19          grune
## 1923                             16.5          grune
## 1924                               18          grune
## 1925                               19          grune
## 1926                               17          grune
## 1927                               20          grune
## 1928                               17          grune
## 1929                               18          grune
## 1930                               16          grune
## 1931                               18          grune
## 1932                               17          grune
## 1933                               18          grune
## 1934                               17          grune
## 1935                               17          grune
## 1936                               17          grune
## 1937                               20          grune
## 1938                             17.5          grune
## 1939                               19          grune
## 1940                             17.5          grune
## 1941                             17.5          grune
## 1942                               19          grune
## 1943                               18          grune
## 1944                               19          grune
## 1945                               20          grune
## 1946                               21          grune
## 1947                             17.5          grune
## 1948                               20          grune
## 1949                               18          grune
## 1950                               19          grune
## 1951                               21          grune
## 1952                               22          grune
## 1953                               18          grune
## 1954                               20          grune
## 1955                               20          grune
## 1956                               18          grune
## 1957                               21          grune
## 1958                               21          grune
## 1959                               18          grune
## 1960                               19          grune
## 1961                               16          grune
## 1962                             17.5          grune
## 1963                               21          grune
## 1964                               18          grune
## 1965                             19.5          grune
## 1966                               19          grune
## 1967                               21          grune
## 1968                               19          grune
## 1969                               18          grune
## 1970                               19          grune
## 1971                               18          grune
## 1972                               20          grune
## 1973                               20          grune
## 1974                               18          grune
## 1975                               20          grune
## 1976                               17          grune
## 1977                               19          grune
## 1978                               17          grune
## 1979                               21          grune
## 1980                               19          grune
## 1981                               18          grune
## 1982                               19          grune
## 1983                               18          grune
## 1984                               20          grune
## 1985                               22          grune
## 1986                               20          grune
## 1987                               19          grune
## 1988                               18          grune
## 1989                               20          grune
## 1990                               21          grune
## 1991                               19          grune
## 1992                               22          grune
## 1993                               21          grune
## 1994                               22          grune
## 1995                               19          grune
## 1996                               21          grune
## 1997                               19          grune
## 1998                               20          grune
## 1999                               21          grune
## 2000                               20          grune
## 2001                               21          grune
## 2002                               20          grune
## 2003                             19.5          grune
## 2004                               21          grune
## 2005                               20          grune
## 2006                               15          grune
## 2007                               20          grune
## 2008                               22          grune
## 2009                               21          grune
## 2010                             21.5          grune
## 2011                               21          grune
## 2012                             20.5          grune
## 2013                               22          grune
## 2014                               21          grune
## 2015                               22          grune
## 2016                               22          grune
## 2017                             21.5          grune
## 2018                               24          grune
## 2019                               22          grune
## 2020                               22          grune
## 2021                               23          grune
## 2022                               23          grune
## 2023                               23          grune
## 2024                               22          grune
## 2025                               25          grune
## 2026                               23          grune
## 2027                               25          grune
## 2028                               23          grune
## 2029                               24          grune
## 2030                               23          grune
## 2031                               26          grune
## 2032                               24          grune
## 2033                               24          grune
## 2034                               24          grune
## 2035                               25          grune
## 2036                               25          grune
## 2037                             23.5          grune
## 2038                               27          grune
## 2039                               21          grune
## 2040                               26          grune
## 2041                               26          grune
## 2042                               26          grune
## 2043                               25          grune
## 2044                               24          grune
## 2045                               28          grune
## 2046                               27          grune
## 2047                               23          grune
## 2048                               25          grune
## 2049                               28          grune
## 2050                               29          grune
## 2051                               23          grune
## 2052                               28          grune
## 2053                               25          grune
## 2054                               28          grune
## 2055                               22          grune
## 2056                               21          grune
## 2057                               23          grune
## 2058                               20          grune
## 2059                               21          grune
## 2060                               23          grune
## 2061                               21          grune
## 2062                               22          grune
## 2063                             20.5          grune
## 2064                               23          grune
## 2065                               22          grune
## 2066                               21          grune
## 2067                               23          grune
## 2068                               23          grune
## 2069                               22          grune
## 2070                               21          grune
## 2071                               21          grune
## 2072                               21          grune
## 2073                               23          grune
## 2074                               23          grune
## 2075                               23          grune
## 2076                               23          grune
## 2077                               20          grune
## 2078                               22          grune
## 2079                             21.5          grune
## 2080                               20          grune
## 2081                               20          grune
## 2082                               22          grune
## 2083                               21          grune
## 2084                               17          grune
## 2085                               21          grune
## 2086                               19          grune
## 2087                               17          grune
## 2088                               18          grune
## 2089                               19          grune
## 2090                               20          grune
## 2091                               17          grune
## 2092                               19          grune
## 2093                               19          grune
## 2094                               18          grune
## 2095                               17          grune
## 2096                               18          grune
## 2097                               19          grune
## 2098                               20          grune
## 2099                               19          grune
## 2100                               20          grune
## 2101                               17          grune
## 2102                               18          grune
## 2103                               19          grune
## 2104                               18          grune
## 2105                             17.5          grune
## 2106                               19          grune
## 2107                               21          grune
## 2108                               19          grune
## 2109                               18          grune
## 2110                               17          grune
## 2111                               19          grune
## 2112                               20          grune
## 2113                               19          grune
## 2114                               17          grune
## 2115                               18          grune
## 2116                               21          grune
## 2117                               20          grune
## 2118                               20          grune
## 2119                               17          grune
## 2120                             17.5          grune
## 2121                               19          grune
## 2122                               20          grune
## 2123                               20          grune
## 2124                               18          grune
## 2125                               18          grune
## 2126                               20          grune
## 2127                               21          grune
## 2128                               18          grune
## 2129                               18          grune
## 2130                               18          grune
## 2131                                –             fw
## 2132                                –             fw
## 2133                                –             fw
## 2134                                –             fw
## 2135                                –             fw
## 2136                                –             fw
## 2137                                –             fw
## 2138                                –             fw
## 2139                                –             fw
## 2140                                3             fw
## 2141                                –             fw
## 2142                                –             fw
## 2143                                –             fw
## 2144                                –             fw
## 2145                                –             fw
## 2146                                –             fw
## 2147                                –             fw
## 2148                                –             fw
## 2149                                –             fw
## 2150                                –             fw
## 2151                                –             fw
## 2152                                –             fw
## 2153                                –             fw
## 2154                                –             fw
## 2155                                –             fw
## 2156                                –             fw
## 2157                                –             fw
## 2158                                –             fw
## 2159                                –             fw
## 2160                              3.5             fw
## 2161                                –             fw
## 2162                                –             fw
## 2163                                –             fw
## 2164                                –             fw
## 2165                                –             fw
## 2166                                –             fw
## 2167                                –             fw
## 2168                                –             fw
## 2169                                –             fw
## 2170                                3             fw
## 2171                                –             fw
## 2172                                –             fw
## 2173                                –             fw
## 2174                                –             fw
## 2175                                –             fw
## 2176                                –             fw
## 2177                                –             fw
## 2178                                –             fw
## 2179                                3             fw
## 2180                                –             fw
## 2181                                –             fw
## 2182                                –             fw
## 2183                                –             fw
## 2184                                –             fw
## 2185                                –             fw
## 2186                                –             fw
## 2187                                –             fw
## 2188                                –             fw
## 2189                                –             fw
## 2190                                –             fw
## 2191                                –             fw
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## 2193                                –             fw
## 2194                                –             fw
## 2195                                –             fw
## 2196                                –             fw
## 2197                                –             fw
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## 2199                                –             fw
## 2200                                –             fw
## 2201                                –             fw
## 2202                                –             fw
## 2203                                –             fw
## 2204                                –             fw
## 2205                                –             fw
## 2206                                –             fw
## 2207                                –             fw
## 2208                                –             fw
## 2209                                –             fw
## 2210                                –             fw
## 2211                                –             fw
## 2212                                –             fw
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## 2214                                –             fw
## 2215                                –             fw
## 2216                                –             fw
## 2217                                –             fw
## 2218                                –             fw
## 2219                                –             fw
## 2220                                –             fw
## 2221                                3             fw
## 2222                                –             fw
## 2223                                –             fw
## 2224                                –             fw
## 2225                                –             fw
## 2226                                –             fw
## 2227                                –             fw
## 2228                                –             fw
## 2229                                –             fw
## 2230                                –             fw
## 2231                                –             fw
## 2232                                –             fw
## 2233                                –             fw
## 2234                                –             fw
## 2235                                –             fw
## 2236                                –             fw
## 2237                                –             fw
## 2238                                –             fw
## 2239                                –             fw
## 2240                                3             fw
## 2241                                –             fw
## 2242                                –             fw
## 2243                                –             fw
## 2244                                –             fw
## 2245                                –             fw
## 2246                                –             fw
## 2247                                –             fw
## 2248                                –             fw
## 2249                                –             fw
## 2250                                –             fw
## 2251                                –             fw
## 2252                                –             fw
## 2253                                –             fw
## 2254                                3             fw
## 2255                                –             fw
## 2256                                –             fw
## 2257                                –             fw
## 2258                                –             fw
## 2259                                –             fw
## 2260                                –             fw
## 2261                                –             fw
## 2262                                –             fw
## 2263                                –             fw
## 2264                                –             fw
## 2265                                –             fw
## 2266                                –             fw
## 2267                                –             fw
## 2268                                –             fw
## 2269                                –             fw
## 2270                                –             fw
## 2271                                –             fw
## 2272                                –             fw
## 2273                                –             fw
## 2274                                –             fw
## 2275                                –             fw
## 2276                                –             fw
## 2277                                –             fw
## 2278                                –             fw
## 2279                                –             fw
## 2280                                –             fw
## 2281                                –             fw
## 2282                                –             fw
## 2283                                –             fw
## 2284                                –             fw
## 2285                                –             fw
## 2286                                –             fw
## 2287                                –             fw
## 2288                                –             fw
## 2289                                –             fw
## 2290                                –             fw
## 2291                                –             fw
## 2292                                –             fw
## 2293                                –             fw
## 2294                                –             fw
## 2295                                –             fw
## 2296                                –             fw
## 2297                                –             fw
## 2298                                –             fw
## 2299                                –             fw
## 2300                                –             fw
## 2301                                –             fw
## 2302                                –             fw
## 2303                                –             fw
## 2304                                –             fw
## 2305                                –             fw
## 2306                                –             fw
## 2307                                –             fw
## 2308                                –             fw
## 2309                                –             fw
## 2310                                –             fw
## 2311                                –             fw
## 2312                                –             fw
## 2313                                –             fw
## 2314                                –             fw
## 2315                                –             fw
## 2316                                –             fw
## 2317                                –             fw
## 2318                                –             fw
## 2319                                –             fw
## 2320                                –             fw
## 2321                                –             fw
## 2322                                –             fw
## 2323                                –             fw
## 2324                                –             fw
## 2325                                –             fw
## 2326                                –             fw
## 2327                                –             fw
## 2328                                –             fw
## 2329                                –             fw
## 2330                                –             fw
## 2331                                –             fw
## 2332                                –             fw
## 2333                                –             fw
## 2334                                –             fw
## 2335                                –             fw
## 2336                                –             fw
## 2337                                –             fw
## 2338                                –             fw
## 2339                                –             fw
## 2340                                –             fw
## 2341                                –             fw
## 2342                                –             fw
## 2343                                –             fw
## 2344                                7         others
## 2345                                8         others
## 2346                                8         others
## 2347                                8         others
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## 2349                                7         others
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## 2358                                6         others
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## 2364                              9.5         others
## 2365                                8         others
## 2366                                6         others
## 2367                              7.5         others
## 2368                                9         others
## 2369                                4         others
## 2370                                7         others
## 2371                                8         others
## 2372                                8         others
## 2373                              5.5         others
## 2374                                9         others
## 2375                                8         others
## 2376                                8         others
## 2377                                8         others
## 2378                                6         others
## 2379                              5.5         others
## 2380                                9         others
## 2381                                7         others
## 2382                                8         others
## 2383                                4         others
## 2384                                6         others
## 2385                                8         others
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## 2391                                6         others
## 2392                                5         others
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## 2397                                7         others
## 2398                                8         others
## 2399                                6         others
## 2400                                6         others
## 2401                                7         others
## 2402                              7.5         others
## 2403                                8         others
## 2404                                7         others
## 2405                                5         others
## 2406                                7         others
## 2407                                7         others
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## 2409                                8         others
## 2410                                8         others
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## 2412                                8         others
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## 2418                                8         others
## 2419                                7         others
## 2420                                5         others
## 2421                                8         others
## 2422                                7         others
## 2423                              5.5         others
## 2424                                6         others
## 2425                                7         others
## 2426                                6         others
## 2427                                5         others
## 2428                                7         others
## 2429                                6         others
## 2430                                7         others
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## 2440                                6         others
## 2441                                5         others
## 2442                                7         others
## 2443                              6.5         others
## 2444                                8         others
## 2445                                5         others
## 2446                              5.5         others
## 2447                                6         others
## 2448                                5         others
## 2449                                6         others
## 2450                                7         others
## 2451                                8         others
## 2452                                5         others
## 2453                                5         others
## 2454                                7         others
## 2455                                6         others
## 2456                              5.5         others
## 2457                                8         others
## 2458                                6         others
## 2459                                5         others
## 2460                                6         others
## 2461                                6         others
## 2462                                6         others
## 2463                              5.5         others
## 2464                                7         others
## 2465                                7         others
## 2466                                7         others
## 2467                                8         others
## 2468                                6         others
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## 2481                                5         others
## 2482                                5         others
## 2483                                7         others
## 2484                                4         others
## 2485                                7         others
## 2486                              5.5         others
## 2487                                7         others
## 2488                                6         others
## 2489                                6         others
## 2490                                7         others
## 2491                                7         others
## 2492                                6         others
## 2493                                8         others
## 2494                                7         others
## 2495                                8         others
## 2496                              6.5         others
## 2497                                6         others
## 2498                                7         others
## 2499                                7         others
## 2500                                6         others
## 2501                                6         others
## 2502                                7         others
## 2503                              5.5         others
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## 2505                                5         others
## 2506                                5         others
## 2507                                7         others
## 2508                                6         others
## 2509                                7         others
## 2510                              6.5         others
## 2511                                8         others
## 2512                                7         others
## 2513                                5         others
## 2514                                7         others
## 2515                                5         others
## 2516                                6         others
## 2517                              4.5         others
## 2518                                7         others
## 2519                                6         others
## 2520                                6         others
## 2521                              5.5         others
## 2522                                6         others
## 2523                                7         others
## 2524                                6         others
## 2525                                6         others
## 2526                              4.5         others
## 2527                                4         others
## 2528                                5         others
## 2529                                7         others
## 2530                                5         others
## 2531                                4         others
## 2532                                7         others
## 2533                                6         others
## 2534                                6         others
## 2535                                5         others
## 2536                                5         others
## 2537                                7         others
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german_election_polls %>% 
  ggplot() +
  # union
  geom_point(aes(x=end_date,y=union, colour="UNION"),shape=21)+
  # span=0.2 to make the line less smoothed
  geom_smooth(aes(x=end_date,y=union, colour="UNION"),se=F,span = 0.2)+ 
  # spd
  geom_point(aes(x=end_date,y=spd, colour="SPD"),shape=21)+
  geom_smooth(aes(x=end_date,y=spd,colour="SPD"),se=F,span = 0.2)+
  # afd
  geom_point(aes(x=end_date,y=af_d,colour="AFD"),shape=21)+
  geom_smooth(aes(x=end_date,y=af_d,colour="AFD"),se=F,span = 0.2)+
  #fdp
  geom_point(aes(x=end_date,y=fdp,colour="FDP"),shape=21)+
  geom_smooth(aes(x=end_date,y=fdp,colour="FDP"),se=F,span = 0.2)+
  #grune
  geom_point(aes(x=end_date,y=grune, colour="GRUNE"),shape=21)+
  geom_smooth(aes(x=end_date,y=grune,colour="GRUNE"),se=F,span = 0.2)+
  #linke
  geom_point(aes(x=end_date,y=linke,colour="LINKE"),shape=21)+
  geom_smooth(aes(x=end_date,y=linke,colour="LINKE"),se=F,span = 0.2)+
  #display every month
  scale_x_date(date_labels="%b %y",date_breaks  ="1 month")+
  labs(
    x="Date",
    y="Votes %"
  )+
  scale_colour_manual("", 
                      breaks = c("UNION","SPD","AFD","FDP","GRUNE","LINKE"),
                      values = c("black","red","blue","yellow","dark green","purple"))+
  NULL

?scale_colour_manual

7 Deliverables

There is a lot of explanatory text, comments, etc. You do not need these, so delete them and produce a stand-alone document that you could share with someone. Knit the edited and completed R Markdown file as an HTML document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.

8 Details

• Who did you collaborate with: Alex Kubbinga, Clara, Jean Huang, Raghav Mehta, Raina Doshi, Yuan Gao
• Approximately how much time did you spend on this problem set: 8 hours
• What, if anything, gave you the most trouble: ANSWER HERE

Please seek out help when you need it, and remember the 15-minute rule. You know enough R (and have enough examples of code from class and your readings) to be able to do this. If you get stuck, ask for help from others, post a question on Slack– and remember that I am here to help too!

As a true test to yourself, do you understand the code you submitted and are you able to explain it to someone else?

9 Rubric

Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analyzed.

Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow (both the code and the output).

Check plus (5/5): Finished all components of the assignment correctly and addressed both challenges. Code is well-documented (both self-documented and with additional comments as necessary). Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.